EvoloPy.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/7ossam81/EvoloPy/blob/master/EvoloPy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IiNG95DSmRmt"
      },
      "source": [
        "<h1>EvoloPy</h1>\n",
        "An open source nature-inspired optimization toolbox for global optimization in Python"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hKzyadcdm6yw"
      },
      "source": [
        "The EvoloPy toolbox provides classical and recent nature-inspired metaheuristic for the global optimization. The list of optimizers that have been implemented includes Particle Swarm Optimization (PSO), Multi-Verse Optimizer (MVO), Grey Wolf Optimizer (GWO), and Moth Flame Optimization (MFO)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2zQnPqDUujil"
      },
      "source": [
        "The full list of implemented optimizers is available here https://github.com/7ossam81/EvoloPy/wiki/List-of-optimizers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ehxW3t0puwpm"
      },
      "source": [
        "<h2>Features</h2>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TufeO-Rturq8"
      },
      "source": [
        "*   Fourteen nature-inspired metaheuristic optimizers are implemented.\n",
        "*   The implimentation uses the fast array manipulation using [NumPy] (http://www.numpy.org/).\n",
        "*   Matrix support using [SciPy's] (https://www.scipy.org/) package.\n",
        "*   More optimizers are comming soon"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oG2TRdx-vf8m"
      },
      "source": [
        "<h2>Installation</h2>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_XpMUOZNvi9H"
      },
      "source": [
        "Python 3.xx is required."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f_PVAZC6v7px"
      },
      "source": [
        "<h2>GitHub</h2>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aW_66ovkv2ev"
      },
      "source": [
        "Clone the Git repository from GitHub:\n",
        "git clone https://github.com/7ossam81/EvoloPy.git"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QgrmE2wEyC_X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd079a4e-7694-405a-f438-742180b650c4"
      },
      "source": [
        "!git clone https://github.com/7ossam81/EvoloPy.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'EvoloPy'...\n",
            "remote: Enumerating objects: 605, done.\u001b[K\n",
            "remote: Counting objects: 100% (151/151), done.\u001b[K\n",
            "remote: Compressing objects: 100% (60/60), done.\u001b[K\n",
            "remote: Total 605 (delta 117), reused 100 (delta 90), pack-reused 454\u001b[K\n",
            "Receiving objects: 100% (605/605), 208.27 KiB | 12.25 MiB/s, done.\n",
            "Resolving deltas: 100% (340/340), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_yVUbtjcyiZR"
      },
      "source": [
        "# Change working directory\n",
        "import os\n",
        "os.chdir(\"EvoloPy/\")"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S4j-t-nXxaM7"
      },
      "source": [
        "<h2>Install Packages</h2>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-mG8vIuIW1n8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f01d390b-cf10-4f9a-fce9-8f1e4e4f0a92"
      },
      "source": [
        "#Install NumPy, SciPy, sklearn, pandas, and matplotlib\n",
        "!pip install -r requirements.txt"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: NumPy in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 1)) (1.25.2)\n",
            "Requirement already satisfied: SciPy in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 2)) (1.11.4)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 3)) (1.2.2)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 4)) (2.0.3)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 5)) (3.7.1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->-r requirements.txt (line 3)) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->-r requirements.txt (line 3)) (3.5.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->-r requirements.txt (line 4)) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->-r requirements.txt (line 4)) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->-r requirements.txt (line 4)) (2024.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->-r requirements.txt (line 5)) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->-r requirements.txt (line 5)) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->-r requirements.txt (line 5)) (4.51.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->-r requirements.txt (line 5)) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->-r requirements.txt (line 5)) (24.0)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->-r requirements.txt (line 5)) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->-r requirements.txt (line 5)) (3.1.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->-r requirements.txt (line 4)) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fEon-wGyZuq4"
      },
      "source": [
        "<h2>User Preferences</h2>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cFF9ioDfyppV"
      },
      "source": [
        "# Select optimizers\n",
        "# \"SSA\",\"PSO\",\"GA\",\"BAT\",\"FFA\",\"GWO\",\"WOA\",\"MVO\",\"MFO\",\"CS\",\"HHO\",\"SCA\",\"JAYA\",\"DE\"\n",
        "optimizer=[\"GA\", \"PSO\", \"GWO\", \"MVO\", \"WOA\"]"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SuWo96bB6ohe"
      },
      "source": [
        "# Select benchmark function\"\n",
        "# \"F1\",\"F2\",\"F3\",\"F4\",\"F5\",\"F6\",\"F7\",\"F8\",\"F9\",\"F10\",\"F11\",\"F12\",\"F13\",\"F14\",\"F15\",\"F16\",\"F17\",\"F18\",\"F19\"\n",
        "objectivefunc=[\"F3\",\"F4\"]"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CQyWvdS3Ipgt"
      },
      "source": [
        "# Select number of repetitions for each experiment.\n",
        "# To obtain meaningful statistical results, usually 30 independent runs are executed for each algorithm.\n",
        "NumOfRuns=3"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2djrIRh26sKB"
      },
      "source": [
        "# Select general parameters for all optimizers (population size, number of iterations) ....\n",
        "params = {'PopulationSize' : 30, 'Iterations' : 50}"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aJb075Np62GM"
      },
      "source": [
        "#Choose whether to Export the results in different formats\n",
        "export_flags = {'Export_avg':True, 'Export_details':True,\n",
        "'Export_convergence':True, 'Export_boxplot':True}"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KAgXf1QkZD7X"
      },
      "source": [
        "<h2>Run Framework</h2>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "APzDcxiwI8A2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5313e845-ac77-4891-a797-28df97079985"
      },
      "source": [
        "# Run EvoCluster\n",
        "from optimizer import run\n",
        "run(optimizer, objectivefunc, NumOfRuns, params, export_flags)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GA is optimizing  \"F3\"\n",
            "['At iteration 1 the best fitness is 130570.16641899163']\n",
            "['At iteration 2 the best fitness is 130570.16641899163']\n",
            "['At iteration 3 the best fitness is 130570.16641899163']\n",
            "['At iteration 4 the best fitness is 130570.16641899163']\n",
            "['At iteration 5 the best fitness is 130570.16641899163']\n",
            "['At iteration 6 the best fitness is 130570.16641899163']\n",
            "['At iteration 7 the best fitness is 130570.16641899163']\n",
            "['At iteration 8 the best fitness is 130570.16641899163']\n",
            "['At iteration 9 the best fitness is 130570.16641899163']\n",
            "['At iteration 10 the best fitness is 130570.16641899163']\n",
            "['At iteration 11 the best fitness is 130570.16641899163']\n",
            "['At iteration 12 the best fitness is 130570.16641899163']\n",
            "['At iteration 13 the best fitness is 81259.69289374947']\n",
            "['At iteration 14 the best fitness is 81259.69289374947']\n",
            "['At iteration 15 the best fitness is 81259.69289374947']\n",
            "['At iteration 16 the best fitness is 81259.69289374947']\n",
            "['At iteration 17 the best fitness is 81259.69289374947']\n",
            "['At iteration 18 the best fitness is 81259.69289374947']\n",
            "['At iteration 19 the best fitness is 81259.69289374947']\n",
            "['At iteration 20 the best fitness is 81259.69289374947']\n",
            "['At iteration 21 the best fitness is 81259.69289374947']\n",
            "['At iteration 22 the best fitness is 81259.69289374947']\n",
            "['At iteration 23 the best fitness is 81259.69289374947']\n",
            "['At iteration 24 the best fitness is 81259.69289374947']\n",
            "['At iteration 25 the best fitness is 81259.69289374947']\n",
            "['At iteration 26 the best fitness is 81259.69289374947']\n",
            "['At iteration 27 the best fitness is 81259.69289374947']\n",
            "['At iteration 28 the best fitness is 81259.69289374947']\n",
            "['At iteration 29 the best fitness is 81259.69289374947']\n",
            "['At iteration 30 the best fitness is 81259.69289374947']\n",
            "['At iteration 31 the best fitness is 81259.69289374947']\n",
            "['At iteration 32 the best fitness is 81259.69289374947']\n",
            "['At iteration 33 the best fitness is 81259.69289374947']\n",
            "['At iteration 34 the best fitness is 81259.69289374947']\n",
            "['At iteration 35 the best fitness is 81259.69289374947']\n",
            "['At iteration 36 the best fitness is 81259.69289374947']\n",
            "['At iteration 37 the best fitness is 81259.69289374947']\n",
            "['At iteration 38 the best fitness is 81259.69289374947']\n",
            "['At iteration 39 the best fitness is 81259.69289374947']\n",
            "['At iteration 40 the best fitness is 81259.69289374947']\n",
            "['At iteration 41 the best fitness is 81259.69289374947']\n",
            "['At iteration 42 the best fitness is 81259.69289374947']\n",
            "['At iteration 43 the best fitness is 81259.69289374947']\n",
            "['At iteration 44 the best fitness is 81259.69289374947']\n",
            "['At iteration 45 the best fitness is 81259.69289374947']\n",
            "['At iteration 46 the best fitness is 81259.69289374947']\n",
            "['At iteration 47 the best fitness is 81259.69289374947']\n",
            "['At iteration 48 the best fitness is 81259.69289374947']\n",
            "['At iteration 49 the best fitness is 81259.69289374947']\n",
            "['At iteration 50 the best fitness is 81259.69289374947']\n",
            "GA is optimizing  \"F3\"\n",
            "['At iteration 1 the best fitness is 206974.08823876557']\n",
            "['At iteration 2 the best fitness is 180538.07305336386']\n",
            "['At iteration 3 the best fitness is 180538.07305336386']\n",
            "['At iteration 4 the best fitness is 180538.07305336386']\n",
            "['At iteration 5 the best fitness is 180538.07305336386']\n",
            "['At iteration 6 the best fitness is 180538.07305336386']\n",
            "['At iteration 7 the best fitness is 176743.67573295135']\n",
            "['At iteration 8 the best fitness is 176743.67573295135']\n",
            "['At iteration 9 the best fitness is 176743.67573295135']\n",
            "['At iteration 10 the best fitness is 176743.67573295135']\n",
            "['At iteration 11 the best fitness is 174192.99802626937']\n",
            "['At iteration 12 the best fitness is 96840.60188082446']\n",
            "['At iteration 13 the best fitness is 96840.60188082446']\n",
            "['At iteration 14 the best fitness is 96840.60188082446']\n",
            "['At iteration 15 the best fitness is 96840.60188082446']\n",
            "['At iteration 16 the best fitness is 96840.60188082446']\n",
            "['At iteration 17 the best fitness is 96840.60188082446']\n",
            "['At iteration 18 the best fitness is 96840.60188082446']\n",
            "['At iteration 19 the best fitness is 96840.60188082446']\n",
            "['At iteration 20 the best fitness is 96840.60188082446']\n",
            "['At iteration 21 the best fitness is 96840.60188082446']\n",
            "['At iteration 22 the best fitness is 96840.60188082446']\n",
            "['At iteration 23 the best fitness is 96840.60188082446']\n",
            "['At iteration 24 the best fitness is 96840.60188082446']\n",
            "['At iteration 25 the best fitness is 96840.60188082446']\n",
            "['At iteration 26 the best fitness is 96840.60188082446']\n",
            "['At iteration 27 the best fitness is 96840.60188082446']\n",
            "['At iteration 28 the best fitness is 96840.60188082446']\n",
            "['At iteration 29 the best fitness is 96840.60188082446']\n",
            "['At iteration 30 the best fitness is 96840.60188082446']\n",
            "['At iteration 31 the best fitness is 96840.60188082446']\n",
            "['At iteration 32 the best fitness is 96840.60188082446']\n",
            "['At iteration 33 the best fitness is 96840.60188082446']\n",
            "['At iteration 34 the best fitness is 96840.60188082446']\n",
            "['At iteration 35 the best fitness is 96840.60188082446']\n",
            "['At iteration 36 the best fitness is 96840.60188082446']\n",
            "['At iteration 37 the best fitness is 96840.60188082446']\n",
            "['At iteration 38 the best fitness is 96840.60188082446']\n",
            "['At iteration 39 the best fitness is 96840.60188082446']\n",
            "['At iteration 40 the best fitness is 96840.60188082446']\n",
            "['At iteration 41 the best fitness is 96840.60188082446']\n",
            "['At iteration 42 the best fitness is 96840.60188082446']\n",
            "['At iteration 43 the best fitness is 96840.60188082446']\n",
            "['At iteration 44 the best fitness is 96840.60188082446']\n",
            "['At iteration 45 the best fitness is 96840.60188082446']\n",
            "['At iteration 46 the best fitness is 96840.60188082446']\n",
            "['At iteration 47 the best fitness is 96840.60188082446']\n",
            "['At iteration 48 the best fitness is 96840.60188082446']\n",
            "['At iteration 49 the best fitness is 96840.60188082446']\n",
            "['At iteration 50 the best fitness is 96840.60188082446']\n",
            "GA is optimizing  \"F3\"\n",
            "['At iteration 1 the best fitness is 168493.21318748093']\n",
            "['At iteration 2 the best fitness is 168493.21318748093']\n",
            "['At iteration 3 the best fitness is 168493.21318748093']\n",
            "['At iteration 4 the best fitness is 168493.21318748093']\n",
            "['At iteration 5 the best fitness is 168493.21318748093']\n",
            "['At iteration 6 the best fitness is 168493.21318748093']\n",
            "['At iteration 7 the best fitness is 168493.21318748093']\n",
            "['At iteration 8 the best fitness is 74258.69216044211']\n",
            "['At iteration 9 the best fitness is 74258.69216044211']\n",
            "['At iteration 10 the best fitness is 74258.69216044211']\n",
            "['At iteration 11 the best fitness is 74258.69216044211']\n",
            "['At iteration 12 the best fitness is 74258.69216044211']\n",
            "['At iteration 13 the best fitness is 74258.69216044211']\n",
            "['At iteration 14 the best fitness is 74258.69216044211']\n",
            "['At iteration 15 the best fitness is 74258.69216044211']\n",
            "['At iteration 16 the best fitness is 74258.69216044211']\n",
            "['At iteration 17 the best fitness is 74258.69216044211']\n",
            "['At iteration 18 the best fitness is 74258.69216044211']\n",
            "['At iteration 19 the best fitness is 74258.69216044211']\n",
            "['At iteration 20 the best fitness is 74258.69216044211']\n",
            "['At iteration 21 the best fitness is 74258.69216044211']\n",
            "['At iteration 22 the best fitness is 74258.69216044211']\n",
            "['At iteration 23 the best fitness is 74258.69216044211']\n",
            "['At iteration 24 the best fitness is 74258.69216044211']\n",
            "['At iteration 25 the best fitness is 74258.69216044211']\n",
            "['At iteration 26 the best fitness is 74258.69216044211']\n",
            "['At iteration 27 the best fitness is 74258.69216044211']\n",
            "['At iteration 28 the best fitness is 74258.69216044211']\n",
            "['At iteration 29 the best fitness is 74258.69216044211']\n",
            "['At iteration 30 the best fitness is 74258.69216044211']\n",
            "['At iteration 31 the best fitness is 74258.69216044211']\n",
            "['At iteration 32 the best fitness is 74258.69216044211']\n",
            "['At iteration 33 the best fitness is 74258.69216044211']\n",
            "['At iteration 34 the best fitness is 74258.69216044211']\n",
            "['At iteration 35 the best fitness is 74258.69216044211']\n",
            "['At iteration 36 the best fitness is 74258.69216044211']\n",
            "['At iteration 37 the best fitness is 74258.69216044211']\n",
            "['At iteration 38 the best fitness is 74258.69216044211']\n",
            "['At iteration 39 the best fitness is 74258.69216044211']\n",
            "['At iteration 40 the best fitness is 74258.69216044211']\n",
            "['At iteration 41 the best fitness is 74258.69216044211']\n",
            "['At iteration 42 the best fitness is 74258.69216044211']\n",
            "['At iteration 43 the best fitness is 74258.69216044211']\n",
            "['At iteration 44 the best fitness is 74258.69216044211']\n",
            "['At iteration 45 the best fitness is 74258.69216044211']\n",
            "['At iteration 46 the best fitness is 74258.69216044211']\n",
            "['At iteration 47 the best fitness is 74258.69216044211']\n",
            "['At iteration 48 the best fitness is 74258.69216044211']\n",
            "['At iteration 49 the best fitness is 74258.69216044211']\n",
            "['At iteration 50 the best fitness is 74258.69216044211']\n",
            "GA is optimizing  \"F4\"\n",
            "['At iteration 1 the best fitness is 91.49311682050431']\n",
            "['At iteration 2 the best fitness is 86.64410244582777']\n",
            "['At iteration 3 the best fitness is 86.64410244582777']\n",
            "['At iteration 4 the best fitness is 86.64410244582777']\n",
            "['At iteration 5 the best fitness is 86.64410244582777']\n",
            "['At iteration 6 the best fitness is 86.64410244582777']\n",
            "['At iteration 7 the best fitness is 86.64410244582777']\n",
            "['At iteration 8 the best fitness is 86.52751197416471']\n",
            "['At iteration 9 the best fitness is 86.52751197416471']\n",
            "['At iteration 10 the best fitness is 86.52751197416471']\n",
            "['At iteration 11 the best fitness is 86.52751197416471']\n",
            "['At iteration 12 the best fitness is 86.52751197416471']\n",
            "['At iteration 13 the best fitness is 86.52751197416471']\n",
            "['At iteration 14 the best fitness is 86.52751197416471']\n",
            "['At iteration 15 the best fitness is 86.52751197416471']\n",
            "['At iteration 16 the best fitness is 84.28167739655974']\n",
            "['At iteration 17 the best fitness is 84.28167739655974']\n",
            "['At iteration 18 the best fitness is 84.28167739655974']\n",
            "['At iteration 19 the best fitness is 84.28167739655974']\n",
            "['At iteration 20 the best fitness is 84.28167739655974']\n",
            "['At iteration 21 the best fitness is 84.28167739655974']\n",
            "['At iteration 22 the best fitness is 84.28167739655974']\n",
            "['At iteration 23 the best fitness is 84.28167739655974']\n",
            "['At iteration 24 the best fitness is 84.28167739655974']\n",
            "['At iteration 25 the best fitness is 84.28167739655974']\n",
            "['At iteration 26 the best fitness is 84.28167739655974']\n",
            "['At iteration 27 the best fitness is 84.28167739655974']\n",
            "['At iteration 28 the best fitness is 84.28167739655974']\n",
            "['At iteration 29 the best fitness is 84.28167739655974']\n",
            "['At iteration 30 the best fitness is 84.28167739655974']\n",
            "['At iteration 31 the best fitness is 84.28167739655974']\n",
            "['At iteration 32 the best fitness is 84.28167739655974']\n",
            "['At iteration 33 the best fitness is 84.28167739655974']\n",
            "['At iteration 34 the best fitness is 84.28167739655974']\n",
            "['At iteration 35 the best fitness is 84.28167739655974']\n",
            "['At iteration 36 the best fitness is 84.28167739655974']\n",
            "['At iteration 37 the best fitness is 83.36831940612885']\n",
            "['At iteration 38 the best fitness is 83.36831940612885']\n",
            "['At iteration 39 the best fitness is 83.36831940612885']\n",
            "['At iteration 40 the best fitness is 83.36831940612885']\n",
            "['At iteration 41 the best fitness is 83.36831940612885']\n",
            "['At iteration 42 the best fitness is 83.36831940612885']\n",
            "['At iteration 43 the best fitness is 83.36831940612885']\n",
            "['At iteration 44 the best fitness is 83.36831940612885']\n",
            "['At iteration 45 the best fitness is 83.36831940612885']\n",
            "['At iteration 46 the best fitness is 83.36831940612885']\n",
            "['At iteration 47 the best fitness is 83.36831940612885']\n",
            "['At iteration 48 the best fitness is 83.36831940612885']\n",
            "['At iteration 49 the best fitness is 83.36831940612885']\n",
            "['At iteration 50 the best fitness is 83.36831940612885']\n",
            "GA is optimizing  \"F4\"\n",
            "['At iteration 1 the best fitness is 89.73773603518218']\n",
            "['At iteration 2 the best fitness is 89.73773603518218']\n",
            "['At iteration 3 the best fitness is 87.01951325983305']\n",
            "['At iteration 4 the best fitness is 87.01951325983305']\n",
            "['At iteration 5 the best fitness is 87.01951325983305']\n",
            "['At iteration 6 the best fitness is 87.01951325983305']\n",
            "['At iteration 7 the best fitness is 87.01951325983305']\n",
            "['At iteration 8 the best fitness is 87.01951325983305']\n",
            "['At iteration 9 the best fitness is 87.01951325983305']\n",
            "['At iteration 10 the best fitness is 87.01951325983305']\n",
            "['At iteration 11 the best fitness is 87.01951325983305']\n",
            "['At iteration 12 the best fitness is 87.01951325983305']\n",
            "['At iteration 13 the best fitness is 87.01951325983305']\n",
            "['At iteration 14 the best fitness is 87.01951325983305']\n",
            "['At iteration 15 the best fitness is 87.01951325983305']\n",
            "['At iteration 16 the best fitness is 87.01951325983305']\n",
            "['At iteration 17 the best fitness is 87.01951325983305']\n",
            "['At iteration 18 the best fitness is 87.01951325983305']\n",
            "['At iteration 19 the best fitness is 87.01951325983305']\n",
            "['At iteration 20 the best fitness is 87.01951325983305']\n",
            "['At iteration 21 the best fitness is 87.01951325983305']\n",
            "['At iteration 22 the best fitness is 87.01951325983305']\n",
            "['At iteration 23 the best fitness is 87.01951325983305']\n",
            "['At iteration 24 the best fitness is 87.01951325983305']\n",
            "['At iteration 25 the best fitness is 87.01951325983305']\n",
            "['At iteration 26 the best fitness is 87.01951325983305']\n",
            "['At iteration 27 the best fitness is 87.01951325983305']\n",
            "['At iteration 28 the best fitness is 87.01951325983305']\n",
            "['At iteration 29 the best fitness is 87.01951325983305']\n",
            "['At iteration 30 the best fitness is 87.01951325983305']\n",
            "['At iteration 31 the best fitness is 87.01951325983305']\n",
            "['At iteration 32 the best fitness is 85.62206517538712']\n",
            "['At iteration 33 the best fitness is 85.62206517538712']\n",
            "['At iteration 34 the best fitness is 85.62206517538712']\n",
            "['At iteration 35 the best fitness is 81.91266743057196']\n",
            "['At iteration 36 the best fitness is 81.91266743057196']\n",
            "['At iteration 37 the best fitness is 81.91266743057196']\n",
            "['At iteration 38 the best fitness is 81.33795230680388']\n",
            "['At iteration 39 the best fitness is 81.33795230680388']\n",
            "['At iteration 40 the best fitness is 81.33795230680388']\n",
            "['At iteration 41 the best fitness is 81.33795230680388']\n",
            "['At iteration 42 the best fitness is 81.33795230680388']\n",
            "['At iteration 43 the best fitness is 81.33795230680388']\n",
            "['At iteration 44 the best fitness is 81.33795230680388']\n",
            "['At iteration 45 the best fitness is 81.33795230680388']\n",
            "['At iteration 46 the best fitness is 81.33795230680388']\n",
            "['At iteration 47 the best fitness is 81.33795230680388']\n",
            "['At iteration 48 the best fitness is 81.33795230680388']\n",
            "['At iteration 49 the best fitness is 81.33795230680388']\n",
            "['At iteration 50 the best fitness is 81.33795230680388']\n",
            "GA is optimizing  \"F4\"\n",
            "['At iteration 1 the best fitness is 90.31643141632807']\n",
            "['At iteration 2 the best fitness is 87.10021103598794']\n",
            "['At iteration 3 the best fitness is 87.10021103598794']\n",
            "['At iteration 4 the best fitness is 86.93368777677767']\n",
            "['At iteration 5 the best fitness is 86.93368777677767']\n",
            "['At iteration 6 the best fitness is 86.93368777677767']\n",
            "['At iteration 7 the best fitness is 86.93368777677767']\n",
            "['At iteration 8 the best fitness is 86.93368777677767']\n",
            "['At iteration 9 the best fitness is 86.93368777677767']\n",
            "['At iteration 10 the best fitness is 86.93368777677767']\n",
            "['At iteration 11 the best fitness is 86.93368777677767']\n",
            "['At iteration 12 the best fitness is 86.93368777677767']\n",
            "['At iteration 13 the best fitness is 86.93368777677767']\n",
            "['At iteration 14 the best fitness is 86.93368777677767']\n",
            "['At iteration 15 the best fitness is 84.47298828290546']\n",
            "['At iteration 16 the best fitness is 84.47298828290546']\n",
            "['At iteration 17 the best fitness is 84.47298828290546']\n",
            "['At iteration 18 the best fitness is 80.87612902281586']\n",
            "['At iteration 19 the best fitness is 76.77173279754619']\n",
            "['At iteration 20 the best fitness is 76.64195608650147']\n",
            "['At iteration 21 the best fitness is 76.64195608650147']\n",
            "['At iteration 22 the best fitness is 76.64195608650147']\n",
            "['At iteration 23 the best fitness is 76.64195608650147']\n",
            "['At iteration 24 the best fitness is 76.64195608650147']\n",
            "['At iteration 25 the best fitness is 76.64195608650147']\n",
            "['At iteration 26 the best fitness is 76.64195608650147']\n",
            "['At iteration 27 the best fitness is 76.64195608650147']\n",
            "['At iteration 28 the best fitness is 76.64195608650147']\n",
            "['At iteration 29 the best fitness is 76.64195608650147']\n",
            "['At iteration 30 the best fitness is 76.64195608650147']\n",
            "['At iteration 31 the best fitness is 73.13476029867635']\n",
            "['At iteration 32 the best fitness is 73.13476029867635']\n",
            "['At iteration 33 the best fitness is 73.13476029867635']\n",
            "['At iteration 34 the best fitness is 73.13476029867635']\n",
            "['At iteration 35 the best fitness is 73.13476029867635']\n",
            "['At iteration 36 the best fitness is 73.13476029867635']\n",
            "['At iteration 37 the best fitness is 73.13476029867635']\n",
            "['At iteration 38 the best fitness is 73.13476029867635']\n",
            "['At iteration 39 the best fitness is 73.13476029867635']\n",
            "['At iteration 40 the best fitness is 73.13476029867635']\n",
            "['At iteration 41 the best fitness is 73.13476029867635']\n",
            "['At iteration 42 the best fitness is 73.13476029867635']\n",
            "['At iteration 43 the best fitness is 73.13476029867635']\n",
            "['At iteration 44 the best fitness is 67.60495757080092']\n",
            "['At iteration 45 the best fitness is 67.60495757080092']\n",
            "['At iteration 46 the best fitness is 67.60495757080092']\n",
            "['At iteration 47 the best fitness is 67.60495757080092']\n",
            "['At iteration 48 the best fitness is 67.60495757080092']\n",
            "['At iteration 49 the best fitness is 67.60495757080092']\n",
            "['At iteration 50 the best fitness is 67.60495757080092']\n",
            "PSO is optimizing  \"F3\"\n",
            "['At iteration 1 the best fitness is 100570.39323303782']\n",
            "['At iteration 2 the best fitness is 100570.39323303782']\n",
            "['At iteration 3 the best fitness is 100570.39323303782']\n",
            "['At iteration 4 the best fitness is 78863.7668877005']\n",
            "['At iteration 5 the best fitness is 60687.38359859974']\n",
            "['At iteration 6 the best fitness is 50019.614951785275']\n",
            "['At iteration 7 the best fitness is 44414.11239069552']\n",
            "['At iteration 8 the best fitness is 42023.49962110052']\n",
            "['At iteration 9 the best fitness is 39805.28045724417']\n",
            "['At iteration 10 the best fitness is 32993.324033308716']\n",
            "['At iteration 11 the best fitness is 30983.78457337045']\n",
            "['At iteration 12 the best fitness is 29179.031050418467']\n",
            "['At iteration 13 the best fitness is 16186.658976059636']\n",
            "['At iteration 14 the best fitness is 16186.658976059636']\n",
            "['At iteration 15 the best fitness is 16186.658976059636']\n",
            "['At iteration 16 the best fitness is 12924.657673819302']\n",
            "['At iteration 17 the best fitness is 12924.657673819302']\n",
            "['At iteration 18 the best fitness is 12924.657673819302']\n",
            "['At iteration 19 the best fitness is 12589.091401671532']\n",
            "['At iteration 20 the best fitness is 11433.823005501079']\n",
            "['At iteration 21 the best fitness is 9089.75642782397']\n",
            "['At iteration 22 the best fitness is 8289.840433619187']\n",
            "['At iteration 23 the best fitness is 7829.391085715777']\n",
            "['At iteration 24 the best fitness is 7235.673217161645']\n",
            "['At iteration 25 the best fitness is 7235.673217161645']\n",
            "['At iteration 26 the best fitness is 7235.673217161645']\n",
            "['At iteration 27 the best fitness is 7082.934903622615']\n",
            "['At iteration 28 the best fitness is 7058.387063130546']\n",
            "['At iteration 29 the best fitness is 6780.494037304629']\n",
            "['At iteration 30 the best fitness is 6671.111687389875']\n",
            "['At iteration 31 the best fitness is 6671.111687389875']\n",
            "['At iteration 32 the best fitness is 6671.111687389875']\n",
            "['At iteration 33 the best fitness is 6671.111687389875']\n",
            "['At iteration 34 the best fitness is 6671.111687389875']\n",
            "['At iteration 35 the best fitness is 6619.643578219076']\n",
            "['At iteration 36 the best fitness is 6297.842332189392']\n",
            "['At iteration 37 the best fitness is 6154.082846916951']\n",
            "['At iteration 38 the best fitness is 5651.703772844006']\n",
            "['At iteration 39 the best fitness is 5617.020784544073']\n",
            "['At iteration 40 the best fitness is 5398.529159560376']\n",
            "['At iteration 41 the best fitness is 5310.821403317641']\n",
            "['At iteration 42 the best fitness is 5068.236368573188']\n",
            "['At iteration 43 the best fitness is 4962.480617400111']\n",
            "['At iteration 44 the best fitness is 4900.331304607711']\n",
            "['At iteration 45 the best fitness is 4859.780326115679']\n",
            "['At iteration 46 the best fitness is 4756.901541566681']\n",
            "['At iteration 47 the best fitness is 4542.767307558965']\n",
            "['At iteration 48 the best fitness is 4542.767307558965']\n",
            "['At iteration 49 the best fitness is 4384.864903041174']\n",
            "['At iteration 50 the best fitness is 4238.733052197284']\n",
            "PSO is optimizing  \"F3\"\n",
            "['At iteration 1 the best fitness is 97804.32925384142']\n",
            "['At iteration 2 the best fitness is 97804.32925384142']\n",
            "['At iteration 3 the best fitness is 97804.32925384142']\n",
            "['At iteration 4 the best fitness is 97804.32925384142']\n",
            "['At iteration 5 the best fitness is 88667.65207605498']\n",
            "['At iteration 6 the best fitness is 66881.93891745737']\n",
            "['At iteration 7 the best fitness is 59277.358551243975']\n",
            "['At iteration 8 the best fitness is 59277.358551243975']\n",
            "['At iteration 9 the best fitness is 59277.358551243975']\n",
            "['At iteration 10 the best fitness is 59277.358551243975']\n",
            "['At iteration 11 the best fitness is 59277.358551243975']\n",
            "['At iteration 12 the best fitness is 48401.812605768304']\n",
            "['At iteration 13 the best fitness is 47693.63924164195']\n",
            "['At iteration 14 the best fitness is 44200.858171693406']\n",
            "['At iteration 15 the best fitness is 35542.077407030025']\n",
            "['At iteration 16 the best fitness is 23938.859986584062']\n",
            "['At iteration 17 the best fitness is 19304.705658174677']\n",
            "['At iteration 18 the best fitness is 19304.705658174677']\n",
            "['At iteration 19 the best fitness is 15520.239629168487']\n",
            "['At iteration 20 the best fitness is 13341.111791824442']\n",
            "['At iteration 21 the best fitness is 13338.766166209654']\n",
            "['At iteration 22 the best fitness is 13232.351612625613']\n",
            "['At iteration 23 the best fitness is 12414.118518165216']\n",
            "['At iteration 24 the best fitness is 12026.103543430201']\n",
            "['At iteration 25 the best fitness is 11524.737592146672']\n",
            "['At iteration 26 the best fitness is 9565.736959267655']\n",
            "['At iteration 27 the best fitness is 8959.728145641508']\n",
            "['At iteration 28 the best fitness is 8174.531954318533']\n",
            "['At iteration 29 the best fitness is 7632.280686099287']\n",
            "['At iteration 30 the best fitness is 7259.012761800846']\n",
            "['At iteration 31 the best fitness is 6663.319635509167']\n",
            "['At iteration 32 the best fitness is 6410.809393321168']\n",
            "['At iteration 33 the best fitness is 6062.682643939875']\n",
            "['At iteration 34 the best fitness is 5772.017867940614']\n",
            "['At iteration 35 the best fitness is 5445.972926362648']\n",
            "['At iteration 36 the best fitness is 5321.25669804658']\n",
            "['At iteration 37 the best fitness is 5321.25669804658']\n",
            "['At iteration 38 the best fitness is 4994.127210959314']\n",
            "['At iteration 39 the best fitness is 4919.946167664301']\n",
            "['At iteration 40 the best fitness is 4746.236778594406']\n",
            "['At iteration 41 the best fitness is 4547.411868954911']\n",
            "['At iteration 42 the best fitness is 4442.291082516597']\n",
            "['At iteration 43 the best fitness is 4159.896201084432']\n",
            "['At iteration 44 the best fitness is 4159.896201084432']\n",
            "['At iteration 45 the best fitness is 4051.697568060805']\n",
            "['At iteration 46 the best fitness is 3890.357786278343']\n",
            "['At iteration 47 the best fitness is 3701.039141445733']\n",
            "['At iteration 48 the best fitness is 3632.463827485012']\n",
            "['At iteration 49 the best fitness is 3514.096501859949']\n",
            "['At iteration 50 the best fitness is 3478.341275039189']\n",
            "PSO is optimizing  \"F3\"\n",
            "['At iteration 1 the best fitness is 77048.80368637636']\n",
            "['At iteration 2 the best fitness is 77048.80368637636']\n",
            "['At iteration 3 the best fitness is 77048.80368637636']\n",
            "['At iteration 4 the best fitness is 60590.25816451179']\n",
            "['At iteration 5 the best fitness is 54788.36768044492']\n",
            "['At iteration 6 the best fitness is 54788.36768044492']\n",
            "['At iteration 7 the best fitness is 49876.958009274385']\n",
            "['At iteration 8 the best fitness is 48060.71467233257']\n",
            "['At iteration 9 the best fitness is 48060.71467233257']\n",
            "['At iteration 10 the best fitness is 43811.68053372477']\n",
            "['At iteration 11 the best fitness is 41168.21725660529']\n",
            "['At iteration 12 the best fitness is 30032.29548080473']\n",
            "['At iteration 13 the best fitness is 24464.89072651111']\n",
            "['At iteration 14 the best fitness is 22436.53358962194']\n",
            "['At iteration 15 the best fitness is 15816.187627666643']\n",
            "['At iteration 16 the best fitness is 13912.038615372969']\n",
            "['At iteration 17 the best fitness is 12293.561536968416']\n",
            "['At iteration 18 the best fitness is 11383.674680959688']\n",
            "['At iteration 19 the best fitness is 11383.674680959688']\n",
            "['At iteration 20 the best fitness is 11269.04878194278']\n",
            "['At iteration 21 the best fitness is 10342.682719787912']\n",
            "['At iteration 22 the best fitness is 9995.623117498813']\n",
            "['At iteration 23 the best fitness is 9429.79669277843']\n",
            "['At iteration 24 the best fitness is 8844.42471050117']\n",
            "['At iteration 25 the best fitness is 8497.953845788166']\n",
            "['At iteration 26 the best fitness is 8205.207823186449']\n",
            "['At iteration 27 the best fitness is 7861.458152542977']\n",
            "['At iteration 28 the best fitness is 7297.1334540912385']\n",
            "['At iteration 29 the best fitness is 7008.5095652810805']\n",
            "['At iteration 30 the best fitness is 6936.097259175041']\n",
            "['At iteration 31 the best fitness is 6760.595275204009']\n",
            "['At iteration 32 the best fitness is 6551.424481379818']\n",
            "['At iteration 33 the best fitness is 6187.74335474338']\n",
            "['At iteration 34 the best fitness is 5925.499857048157']\n",
            "['At iteration 35 the best fitness is 5925.499857048157']\n",
            "['At iteration 36 the best fitness is 5709.75703799788']\n",
            "['At iteration 37 the best fitness is 5481.3456137912835']\n",
            "['At iteration 38 the best fitness is 5481.3456137912835']\n",
            "['At iteration 39 the best fitness is 5453.828242626306']\n",
            "['At iteration 40 the best fitness is 5222.118955623407']\n",
            "['At iteration 41 the best fitness is 5153.081049685055']\n",
            "['At iteration 42 the best fitness is 4944.133430650337']\n",
            "['At iteration 43 the best fitness is 4936.921068795989']\n",
            "['At iteration 44 the best fitness is 4796.089921753061']\n",
            "['At iteration 45 the best fitness is 4713.281682515787']\n",
            "['At iteration 46 the best fitness is 4617.719936973457']\n",
            "['At iteration 47 the best fitness is 4606.011141044458']\n",
            "['At iteration 48 the best fitness is 4606.011141044458']\n",
            "['At iteration 49 the best fitness is 4481.459283772553']\n",
            "['At iteration 50 the best fitness is 4456.756440764844']\n",
            "PSO is optimizing  \"F4\"\n",
            "['At iteration 1 the best fitness is 92.68842047768467']\n",
            "['At iteration 2 the best fitness is 87.09840153760618']\n",
            "['At iteration 3 the best fitness is 81.9490007796033']\n",
            "['At iteration 4 the best fitness is 76.36296214826683']\n",
            "['At iteration 5 the best fitness is 70.7170007796033']\n",
            "['At iteration 6 the best fitness is 65.14768821906472']\n",
            "['At iteration 7 the best fitness is 59.41751795171601']\n",
            "['At iteration 8 the best fitness is 53.76185148069237']\n",
            "['At iteration 9 the best fitness is 47.98257157466287']\n",
            "['At iteration 10 the best fitness is 42.83799365260334']\n",
            "['At iteration 11 the best fitness is 37.660695538782676']\n",
            "['At iteration 12 the best fitness is 32.060174214363535']\n",
            "['At iteration 13 the best fitness is 26.62338341543293']\n",
            "['At iteration 14 the best fitness is 22.321743032925866']\n",
            "['At iteration 15 the best fitness is 18.736768517789674']\n",
            "['At iteration 16 the best fitness is 16.87371678758446']\n",
            "['At iteration 17 the best fitness is 15.925234945422726']\n",
            "['At iteration 18 the best fitness is 15.435701818927763']\n",
            "['At iteration 19 the best fitness is 15.191495624782128']\n",
            "['At iteration 20 the best fitness is 14.258409735720267']\n",
            "['At iteration 21 the best fitness is 13.552501961974679']\n",
            "['At iteration 22 the best fitness is 11.829352344469644']\n",
            "['At iteration 23 the best fitness is 10.243566450599452']\n",
            "['At iteration 24 the best fitness is 8.677475784627108']\n",
            "['At iteration 25 the best fitness is 8.06829705429466']\n",
            "['At iteration 26 the best fitness is 8.06829705429466']\n",
            "['At iteration 27 the best fitness is 8.002374086398039']\n",
            "['At iteration 28 the best fitness is 7.620671084595615']\n",
            "['At iteration 29 the best fitness is 7.512000917189323']\n",
            "['At iteration 30 the best fitness is 7.512000917189323']\n",
            "['At iteration 31 the best fitness is 7.473805520003588']\n",
            "['At iteration 32 the best fitness is 7.473805520003588']\n",
            "['At iteration 33 the best fitness is 7.320801159241654']\n",
            "['At iteration 34 the best fitness is 7.320801159241654']\n",
            "['At iteration 35 the best fitness is 7.320801159241654']\n",
            "['At iteration 36 the best fitness is 7.272002956205682']\n",
            "['At iteration 37 the best fitness is 7.246715760356222']\n",
            "['At iteration 38 the best fitness is 7.239516415838867']\n",
            "['At iteration 39 the best fitness is 7.1091647980298145']\n",
            "['At iteration 40 the best fitness is 7.1091647980298145']\n",
            "['At iteration 41 the best fitness is 7.06408263213793']\n",
            "['At iteration 42 the best fitness is 7.06408263213793']\n",
            "['At iteration 43 the best fitness is 6.96575961962604']\n",
            "['At iteration 44 the best fitness is 6.96575961962604']\n",
            "['At iteration 45 the best fitness is 6.96575961962604']\n",
            "['At iteration 46 the best fitness is 6.96575961962604']\n",
            "['At iteration 47 the best fitness is 6.85183175458842']\n",
            "['At iteration 48 the best fitness is 6.839254007429885']\n",
            "['At iteration 49 the best fitness is 6.621876515316782']\n",
            "['At iteration 50 the best fitness is 6.601375961188286']\n",
            "PSO is optimizing  \"F4\"\n",
            "['At iteration 1 the best fitness is 89.53581360963985']\n",
            "['At iteration 2 the best fitness is 89.09984197889816']\n",
            "['At iteration 3 the best fitness is 83.39047203735072']\n",
            "['At iteration 4 the best fitness is 77.53581360963985']\n",
            "['At iteration 5 the best fitness is 71.94028320697257']\n",
            "['At iteration 6 the best fitness is 66.15847203735072']\n",
            "['At iteration 7 the best fitness is 60.29169792827361']\n",
            "['At iteration 8 the best fitness is 54.872751745621514']\n",
            "['At iteration 9 the best fitness is 49.55215943201132']\n",
            "['At iteration 10 the best fitness is 44.89105264675509']\n",
            "['At iteration 11 the best fitness is 39.37840113362338']\n",
            "['At iteration 12 the best fitness is 35.845361547992105']\n",
            "['At iteration 13 the best fitness is 30.23044809620872']\n",
            "['At iteration 14 the best fitness is 26.119947182414023']\n",
            "['At iteration 15 the best fitness is 23.99513999496541']\n",
            "['At iteration 16 the best fitness is 20.16879082604219']\n",
            "['At iteration 17 the best fitness is 18.823672123791866']\n",
            "['At iteration 18 the best fitness is 18.580380748766554']\n",
            "['At iteration 19 the best fitness is 17.59525157442173']\n",
            "['At iteration 20 the best fitness is 17.332055708654437']\n",
            "['At iteration 21 the best fitness is 17.176985639021222']\n",
            "['At iteration 22 the best fitness is 16.365748634542925']\n",
            "['At iteration 23 the best fitness is 16.365748634542925']\n",
            "['At iteration 24 the best fitness is 15.140859077228104']\n",
            "['At iteration 25 the best fitness is 14.678789477939283']\n",
            "['At iteration 26 the best fitness is 13.92813140016357']\n",
            "['At iteration 27 the best fitness is 13.92813140016357']\n",
            "['At iteration 28 the best fitness is 13.92813140016357']\n",
            "['At iteration 29 the best fitness is 13.92813140016357']\n",
            "['At iteration 30 the best fitness is 13.897904006679859']\n",
            "['At iteration 31 the best fitness is 13.65019221505487']\n",
            "['At iteration 32 the best fitness is 13.301016191702328']\n",
            "['At iteration 33 the best fitness is 13.121238430275465']\n",
            "['At iteration 34 the best fitness is 12.6751705137491']\n",
            "['At iteration 35 the best fitness is 12.6751705137491']\n",
            "['At iteration 36 the best fitness is 12.123298693581742']\n",
            "['At iteration 37 the best fitness is 11.846551680558285']\n",
            "['At iteration 38 the best fitness is 11.583303836557963']\n",
            "['At iteration 39 the best fitness is 11.516997708962144']\n",
            "['At iteration 40 the best fitness is 11.516997708962144']\n",
            "['At iteration 41 the best fitness is 11.516997708962144']\n",
            "['At iteration 42 the best fitness is 11.351656423915584']\n",
            "['At iteration 43 the best fitness is 11.263149370363267']\n",
            "['At iteration 44 the best fitness is 10.771105799295052']\n",
            "['At iteration 45 the best fitness is 10.24401843634312']\n",
            "['At iteration 46 the best fitness is 10.079832872177168']\n",
            "['At iteration 47 the best fitness is 10.079832872177168']\n",
            "['At iteration 48 the best fitness is 10.064990218559181']\n",
            "['At iteration 49 the best fitness is 9.956224819899665']\n",
            "['At iteration 50 the best fitness is 9.956224819899665']\n",
            "PSO is optimizing  \"F4\"\n",
            "['At iteration 1 the best fitness is 90.79397089370873']\n",
            "['At iteration 2 the best fitness is 87.53523892754711']\n",
            "['At iteration 3 the best fitness is 82.98682116658851']\n",
            "['At iteration 4 the best fitness is 77.3530882080039']\n",
            "['At iteration 5 the best fitness is 71.72025325929432']\n",
            "['At iteration 6 the best fitness is 65.95618223530622']\n",
            "['At iteration 7 the best fitness is 60.39059162952239']\n",
            "['At iteration 8 the best fitness is 54.789326548853154']\n",
            "['At iteration 9 the best fitness is 51.11561415301277']\n",
            "['At iteration 10 the best fitness is 45.386506289329446']\n",
            "['At iteration 11 the best fitness is 40.06580005423449']\n",
            "['At iteration 12 the best fitness is 34.1984559092496']\n",
            "['At iteration 13 the best fitness is 29.505800054234484']\n",
            "['At iteration 14 the best fitness is 25.05108018630421']\n",
            "['At iteration 15 the best fitness is 24.246795625492375']\n",
            "['At iteration 16 the best fitness is 20.03459494121425']\n",
            "['At iteration 17 the best fitness is 19.284070276739183']\n",
            "['At iteration 18 the best fitness is 17.686440481685118']\n",
            "['At iteration 19 the best fitness is 17.1739696796162']\n",
            "['At iteration 20 the best fitness is 15.86772234495911']\n",
            "['At iteration 21 the best fitness is 15.186238091661101']\n",
            "['At iteration 22 the best fitness is 13.994074580689254']\n",
            "['At iteration 23 the best fitness is 13.994074580689254']\n",
            "['At iteration 24 the best fitness is 13.613370067531893']\n",
            "['At iteration 25 the best fitness is 13.613370067531893']\n",
            "['At iteration 26 the best fitness is 13.559893305073103']\n",
            "['At iteration 27 the best fitness is 13.559893305073103']\n",
            "['At iteration 28 the best fitness is 13.079762014212378']\n",
            "['At iteration 29 the best fitness is 13.079762014212378']\n",
            "['At iteration 30 the best fitness is 13.079762014212378']\n",
            "['At iteration 31 the best fitness is 12.939133558303515']\n",
            "['At iteration 32 the best fitness is 12.818159938726883']\n",
            "['At iteration 33 the best fitness is 12.767166841351946']\n",
            "['At iteration 34 the best fitness is 11.982757937835697']\n",
            "['At iteration 35 the best fitness is 11.982757937835697']\n",
            "['At iteration 36 the best fitness is 11.982757937835697']\n",
            "['At iteration 37 the best fitness is 11.53295763020959']\n",
            "['At iteration 38 the best fitness is 11.53295763020959']\n",
            "['At iteration 39 the best fitness is 11.53295763020959']\n",
            "['At iteration 40 the best fitness is 11.53295763020959']\n",
            "['At iteration 41 the best fitness is 11.53295763020959']\n",
            "['At iteration 42 the best fitness is 11.489915994762864']\n",
            "['At iteration 43 the best fitness is 11.406231099558012']\n",
            "['At iteration 44 the best fitness is 11.403637388004995']\n",
            "['At iteration 45 the best fitness is 11.27667769012553']\n",
            "['At iteration 46 the best fitness is 11.163298168746849']\n",
            "['At iteration 47 the best fitness is 11.12231555836179']\n",
            "['At iteration 48 the best fitness is 11.111824010103215']\n",
            "['At iteration 49 the best fitness is 10.926795141413278']\n",
            "['At iteration 50 the best fitness is 10.881151641229788']\n",
            "GWO is optimizing  \"F3\"\n",
            "['At iteration 0 the best fitness is 187608.89306926288']\n",
            "['At iteration 1 the best fitness is 124512.89062154118']\n",
            "['At iteration 2 the best fitness is 120735.49636685246']\n",
            "['At iteration 3 the best fitness is 120735.49636685246']\n",
            "['At iteration 4 the best fitness is 101171.08899311168']\n",
            "['At iteration 5 the best fitness is 77360.45212340233']\n",
            "['At iteration 6 the best fitness is 72213.48252906113']\n",
            "['At iteration 7 the best fitness is 72213.48252906113']\n",
            "['At iteration 8 the best fitness is 61079.106070794376']\n",
            "['At iteration 9 the best fitness is 42764.30524166528']\n",
            "['At iteration 10 the best fitness is 42631.1391600624']\n",
            "['At iteration 11 the best fitness is 36923.179703461145']\n",
            "['At iteration 12 the best fitness is 31820.600509803277']\n",
            "['At iteration 13 the best fitness is 20690.876371184877']\n",
            "['At iteration 14 the best fitness is 20690.876371184877']\n",
            "['At iteration 15 the best fitness is 20690.876371184877']\n",
            "['At iteration 16 the best fitness is 12574.935173502996']\n",
            "['At iteration 17 the best fitness is 12574.935173502996']\n",
            "['At iteration 18 the best fitness is 11175.807842934953']\n",
            "['At iteration 19 the best fitness is 10494.377485967336']\n",
            "['At iteration 20 the best fitness is 8547.418506049336']\n",
            "['At iteration 21 the best fitness is 7037.480524437412']\n",
            "['At iteration 22 the best fitness is 6707.614012568397']\n",
            "['At iteration 23 the best fitness is 5492.412910829888']\n",
            "['At iteration 24 the best fitness is 5492.412910829888']\n",
            "['At iteration 25 the best fitness is 5395.003662626709']\n",
            "['At iteration 26 the best fitness is 5094.455638387143']\n",
            "['At iteration 27 the best fitness is 3855.6018566503694']\n",
            "['At iteration 28 the best fitness is 3417.753015801517']\n",
            "['At iteration 29 the best fitness is 3077.5730819306564']\n",
            "['At iteration 30 the best fitness is 3077.5730819306564']\n",
            "['At iteration 31 the best fitness is 3077.5730819306564']\n",
            "['At iteration 32 the best fitness is 3077.5730819306564']\n",
            "['At iteration 33 the best fitness is 2910.5052305106524']\n",
            "['At iteration 34 the best fitness is 2766.913100875935']\n",
            "['At iteration 35 the best fitness is 2363.2717882055554']\n",
            "['At iteration 36 the best fitness is 2363.2717882055554']\n",
            "['At iteration 37 the best fitness is 2291.7514315912717']\n",
            "['At iteration 38 the best fitness is 2125.3075304102317']\n",
            "['At iteration 39 the best fitness is 1985.9335168162781']\n",
            "['At iteration 40 the best fitness is 1939.8924773950237']\n",
            "['At iteration 41 the best fitness is 1876.394458239374']\n",
            "['At iteration 42 the best fitness is 1787.108698966315']\n",
            "['At iteration 43 the best fitness is 1718.1533690980523']\n",
            "['At iteration 44 the best fitness is 1650.0996649846795']\n",
            "['At iteration 45 the best fitness is 1634.42462641619']\n",
            "['At iteration 46 the best fitness is 1592.9641783622171']\n",
            "['At iteration 47 the best fitness is 1553.3025564698357']\n",
            "['At iteration 48 the best fitness is 1513.2392518868032']\n",
            "['At iteration 49 the best fitness is 1499.4353164957029']\n",
            "GWO is optimizing  \"F3\"\n",
            "['At iteration 0 the best fitness is 173035.46409534017']\n",
            "['At iteration 1 the best fitness is 53286.11865529688']\n",
            "['At iteration 2 the best fitness is 53286.11865529688']\n",
            "['At iteration 3 the best fitness is 53286.11865529688']\n",
            "['At iteration 4 the best fitness is 53286.11865529688']\n",
            "['At iteration 5 the best fitness is 49236.92385013552']\n",
            "['At iteration 6 the best fitness is 45944.181526206994']\n",
            "['At iteration 7 the best fitness is 45944.181526206994']\n",
            "['At iteration 8 the best fitness is 30008.1021951109']\n",
            "['At iteration 9 the best fitness is 30008.1021951109']\n",
            "['At iteration 10 the best fitness is 29534.89758191487']\n",
            "['At iteration 11 the best fitness is 24172.077894053567']\n",
            "['At iteration 12 the best fitness is 24172.077894053567']\n",
            "['At iteration 13 the best fitness is 24172.077894053567']\n",
            "['At iteration 14 the best fitness is 17979.079757213694']\n",
            "['At iteration 15 the best fitness is 17979.079757213694']\n",
            "['At iteration 16 the best fitness is 17979.079757213694']\n",
            "['At iteration 17 the best fitness is 16200.262104363645']\n",
            "['At iteration 18 the best fitness is 14271.048235362297']\n",
            "['At iteration 19 the best fitness is 14271.048235362297']\n",
            "['At iteration 20 the best fitness is 14271.048235362297']\n",
            "['At iteration 21 the best fitness is 13726.678660235058']\n",
            "['At iteration 22 the best fitness is 10931.72671487']\n",
            "['At iteration 23 the best fitness is 10163.598478154561']\n",
            "['At iteration 24 the best fitness is 8809.068487858636']\n",
            "['At iteration 25 the best fitness is 7859.862023244359']\n",
            "['At iteration 26 the best fitness is 7810.367097026365']\n",
            "['At iteration 27 the best fitness is 7651.177781088497']\n",
            "['At iteration 28 the best fitness is 7067.050126335371']\n",
            "['At iteration 29 the best fitness is 5228.062625464474']\n",
            "['At iteration 30 the best fitness is 5149.6636992657695']\n",
            "['At iteration 31 the best fitness is 4971.774043104198']\n",
            "['At iteration 32 the best fitness is 4753.448050229364']\n",
            "['At iteration 33 the best fitness is 3945.9225849995314']\n",
            "['At iteration 34 the best fitness is 3945.9225849995314']\n",
            "['At iteration 35 the best fitness is 3198.118068728234']\n",
            "['At iteration 36 the best fitness is 3198.118068728234']\n",
            "['At iteration 37 the best fitness is 3198.118068728234']\n",
            "['At iteration 38 the best fitness is 3034.8873273958525']\n",
            "['At iteration 39 the best fitness is 2968.3104116937607']\n",
            "['At iteration 40 the best fitness is 2623.669516706752']\n",
            "['At iteration 41 the best fitness is 2623.669516706752']\n",
            "['At iteration 42 the best fitness is 2514.831216917347']\n",
            "['At iteration 43 the best fitness is 2509.932257005928']\n",
            "['At iteration 44 the best fitness is 2373.582519107556']\n",
            "['At iteration 45 the best fitness is 2360.472669152329']\n",
            "['At iteration 46 the best fitness is 2299.638991349022']\n",
            "['At iteration 47 the best fitness is 2250.9118863611457']\n",
            "['At iteration 48 the best fitness is 2187.788614090906']\n",
            "['At iteration 49 the best fitness is 2176.2204730111553']\n",
            "GWO is optimizing  \"F3\"\n",
            "['At iteration 0 the best fitness is 137770.14894649948']\n",
            "['At iteration 1 the best fitness is 106556.46682431783']\n",
            "['At iteration 2 the best fitness is 102659.13019029368']\n",
            "['At iteration 3 the best fitness is 65414.95793019593']\n",
            "['At iteration 4 the best fitness is 65414.95793019593']\n",
            "['At iteration 5 the best fitness is 37034.40451364076']\n",
            "['At iteration 6 the best fitness is 32758.210487970035']\n",
            "['At iteration 7 the best fitness is 31175.884335064427']\n",
            "['At iteration 8 the best fitness is 29402.73082000401']\n",
            "['At iteration 9 the best fitness is 20269.19310064843']\n",
            "['At iteration 10 the best fitness is 20269.19310064843']\n",
            "['At iteration 11 the best fitness is 17869.4980530864']\n",
            "['At iteration 12 the best fitness is 17869.4980530864']\n",
            "['At iteration 13 the best fitness is 13973.59519325863']\n",
            "['At iteration 14 the best fitness is 13890.160830818515']\n",
            "['At iteration 15 the best fitness is 11705.938098788814']\n",
            "['At iteration 16 the best fitness is 11145.625627545534']\n",
            "['At iteration 17 the best fitness is 11145.625627545534']\n",
            "['At iteration 18 the best fitness is 11145.625627545534']\n",
            "['At iteration 19 the best fitness is 11145.625627545534']\n",
            "['At iteration 20 the best fitness is 8073.198463684852']\n",
            "['At iteration 21 the best fitness is 7359.649681573431']\n",
            "['At iteration 22 the best fitness is 7359.649681573431']\n",
            "['At iteration 23 the best fitness is 7359.649681573431']\n",
            "['At iteration 24 the best fitness is 7359.649681573431']\n",
            "['At iteration 25 the best fitness is 5990.5872899534215']\n",
            "['At iteration 26 the best fitness is 5990.5872899534215']\n",
            "['At iteration 27 the best fitness is 5990.5872899534215']\n",
            "['At iteration 28 the best fitness is 5550.296947199281']\n",
            "['At iteration 29 the best fitness is 5155.520989592472']\n",
            "['At iteration 30 the best fitness is 4587.335433323167']\n",
            "['At iteration 31 the best fitness is 4410.737075727475']\n",
            "['At iteration 32 the best fitness is 4037.334765310185']\n",
            "['At iteration 33 the best fitness is 3805.3849715245974']\n",
            "['At iteration 34 the best fitness is 3378.6732563762857']\n",
            "['At iteration 35 the best fitness is 3378.6732563762857']\n",
            "['At iteration 36 the best fitness is 3044.643599852667']\n",
            "['At iteration 37 the best fitness is 3044.643599852667']\n",
            "['At iteration 38 the best fitness is 3012.2821582241154']\n",
            "['At iteration 39 the best fitness is 2829.701900248925']\n",
            "['At iteration 40 the best fitness is 2694.0080444919013']\n",
            "['At iteration 41 the best fitness is 2547.989536527569']\n",
            "['At iteration 42 the best fitness is 2533.6420934441217']\n",
            "['At iteration 43 the best fitness is 2477.3749632234717']\n",
            "['At iteration 44 the best fitness is 2404.71463363713']\n",
            "['At iteration 45 the best fitness is 2302.1393852988635']\n",
            "['At iteration 46 the best fitness is 2275.044290333092']\n",
            "['At iteration 47 the best fitness is 2275.044290333092']\n",
            "['At iteration 48 the best fitness is 2209.0119308124604']\n",
            "['At iteration 49 the best fitness is 2209.0119308124604']\n",
            "GWO is optimizing  \"F4\"\n",
            "['At iteration 0 the best fitness is 83.31631474259873']\n",
            "['At iteration 1 the best fitness is 83.31631474259873']\n",
            "['At iteration 2 the best fitness is 83.31631474259873']\n",
            "['At iteration 3 the best fitness is 78.14728359385471']\n",
            "['At iteration 4 the best fitness is 78.14728359385471']\n",
            "['At iteration 5 the best fitness is 72.27894849515467']\n",
            "['At iteration 6 the best fitness is 58.67487276505208']\n",
            "['At iteration 7 the best fitness is 58.67487276505208']\n",
            "['At iteration 8 the best fitness is 45.8665737861931']\n",
            "['At iteration 9 the best fitness is 44.7053905407875']\n",
            "['At iteration 10 the best fitness is 42.1129007763049']\n",
            "['At iteration 11 the best fitness is 35.45910090102399']\n",
            "['At iteration 12 the best fitness is 34.47156674101873']\n",
            "['At iteration 13 the best fitness is 29.25196849094381']\n",
            "['At iteration 14 the best fitness is 24.10104257739589']\n",
            "['At iteration 15 the best fitness is 23.898954128944098']\n",
            "['At iteration 16 the best fitness is 20.389753614997087']\n",
            "['At iteration 17 the best fitness is 19.470396944953933']\n",
            "['At iteration 18 the best fitness is 19.05429947862794']\n",
            "['At iteration 19 the best fitness is 17.544993747211993']\n",
            "['At iteration 20 the best fitness is 17.544993747211993']\n",
            "['At iteration 21 the best fitness is 16.269346217565822']\n",
            "['At iteration 22 the best fitness is 16.269346217565822']\n",
            "['At iteration 23 the best fitness is 13.777593508169566']\n",
            "['At iteration 24 the best fitness is 13.777593508169566']\n",
            "['At iteration 25 the best fitness is 13.777593508169566']\n",
            "['At iteration 26 the best fitness is 13.714004801221966']\n",
            "['At iteration 27 the best fitness is 11.929323513440252']\n",
            "['At iteration 28 the best fitness is 11.929323513440252']\n",
            "['At iteration 29 the best fitness is 11.929323513440252']\n",
            "['At iteration 30 the best fitness is 10.713594482599644']\n",
            "['At iteration 31 the best fitness is 10.713594482599644']\n",
            "['At iteration 32 the best fitness is 10.149974179253464']\n",
            "['At iteration 33 the best fitness is 9.670704293434584']\n",
            "['At iteration 34 the best fitness is 9.44412503196304']\n",
            "['At iteration 35 the best fitness is 9.239037036788316']\n",
            "['At iteration 36 the best fitness is 8.831555257286654']\n",
            "['At iteration 37 the best fitness is 8.424582646685254']\n",
            "['At iteration 38 the best fitness is 8.34299770195885']\n",
            "['At iteration 39 the best fitness is 8.2728850966553']\n",
            "['At iteration 40 the best fitness is 7.9728578601412226']\n",
            "['At iteration 41 the best fitness is 7.852155891402371']\n",
            "['At iteration 42 the best fitness is 7.852155891402371']\n",
            "['At iteration 43 the best fitness is 7.610395405162131']\n",
            "['At iteration 44 the best fitness is 7.503613445350287']\n",
            "['At iteration 45 the best fitness is 7.459066840228867']\n",
            "['At iteration 46 the best fitness is 7.392620669815458']\n",
            "['At iteration 47 the best fitness is 7.236199891181376']\n",
            "['At iteration 48 the best fitness is 7.177387686827601']\n",
            "['At iteration 49 the best fitness is 7.097498465800875']\n",
            "GWO is optimizing  \"F4\"\n",
            "['At iteration 0 the best fitness is 88.65569050233466']\n",
            "['At iteration 1 the best fitness is 88.65569050233466']\n",
            "['At iteration 2 the best fitness is 87.02967943598894']\n",
            "['At iteration 3 the best fitness is 72.02544079249853']\n",
            "['At iteration 4 the best fitness is 70.22003942203551']\n",
            "['At iteration 5 the best fitness is 57.450970075592785']\n",
            "['At iteration 6 the best fitness is 53.24120292322274']\n",
            "['At iteration 7 the best fitness is 52.44372884247587']\n",
            "['At iteration 8 the best fitness is 52.44372884247587']\n",
            "['At iteration 9 the best fitness is 52.44372884247587']\n",
            "['At iteration 10 the best fitness is 44.375954120564224']\n",
            "['At iteration 11 the best fitness is 44.375954120564224']\n",
            "['At iteration 12 the best fitness is 41.085300731173454']\n",
            "['At iteration 13 the best fitness is 38.219071024037156']\n",
            "['At iteration 14 the best fitness is 38.219071024037156']\n",
            "['At iteration 15 the best fitness is 38.102466797762354']\n",
            "['At iteration 16 the best fitness is 31.83212031888524']\n",
            "['At iteration 17 the best fitness is 31.83212031888524']\n",
            "['At iteration 18 the best fitness is 29.12013768133365']\n",
            "['At iteration 19 the best fitness is 29.12013768133365']\n",
            "['At iteration 20 the best fitness is 27.793515471178768']\n",
            "['At iteration 21 the best fitness is 26.305454223507564']\n",
            "['At iteration 22 the best fitness is 25.086301034426018']\n",
            "['At iteration 23 the best fitness is 24.039125520944495']\n",
            "['At iteration 24 the best fitness is 24.039125520944495']\n",
            "['At iteration 25 the best fitness is 23.002851293820886']\n",
            "['At iteration 26 the best fitness is 22.24702862520304']\n",
            "['At iteration 27 the best fitness is 21.20630790422802']\n",
            "['At iteration 28 the best fitness is 19.321712449652768']\n",
            "['At iteration 29 the best fitness is 17.99793407516756']\n",
            "['At iteration 30 the best fitness is 16.617859253826616']\n",
            "['At iteration 31 the best fitness is 16.291801674258156']\n",
            "['At iteration 32 the best fitness is 16.291801674258156']\n",
            "['At iteration 33 the best fitness is 15.893202066503397']\n",
            "['At iteration 34 the best fitness is 15.499399179739838']\n",
            "['At iteration 35 the best fitness is 14.909396695754085']\n",
            "['At iteration 36 the best fitness is 13.71166641459341']\n",
            "['At iteration 37 the best fitness is 13.71166641459341']\n",
            "['At iteration 38 the best fitness is 13.545289140590514']\n",
            "['At iteration 39 the best fitness is 12.963496192432197']\n",
            "['At iteration 40 the best fitness is 12.626883127624128']\n",
            "['At iteration 41 the best fitness is 12.230276309677606']\n",
            "['At iteration 42 the best fitness is 11.769404849848817']\n",
            "['At iteration 43 the best fitness is 11.268280486173547']\n",
            "['At iteration 44 the best fitness is 11.268280486173547']\n",
            "['At iteration 45 the best fitness is 11.099773023928327']\n",
            "['At iteration 46 the best fitness is 10.984353691093085']\n",
            "['At iteration 47 the best fitness is 10.605133437891725']\n",
            "['At iteration 48 the best fitness is 10.605133437891725']\n",
            "['At iteration 49 the best fitness is 10.513755854601406']\n",
            "GWO is optimizing  \"F4\"\n",
            "['At iteration 0 the best fitness is 89.17881387444848']\n",
            "['At iteration 1 the best fitness is 81.18904730482829']\n",
            "['At iteration 2 the best fitness is 81.18904730482829']\n",
            "['At iteration 3 the best fitness is 81.18904730482829']\n",
            "['At iteration 4 the best fitness is 81.18904730482829']\n",
            "['At iteration 5 the best fitness is 76.1576851650219']\n",
            "['At iteration 6 the best fitness is 72.98089929952565']\n",
            "['At iteration 7 the best fitness is 67.42769643490351']\n",
            "['At iteration 8 the best fitness is 67.42769643490351']\n",
            "['At iteration 9 the best fitness is 53.88684469281426']\n",
            "['At iteration 10 the best fitness is 53.86709292446179']\n",
            "['At iteration 11 the best fitness is 48.858345031421685']\n",
            "['At iteration 12 the best fitness is 42.567024002582826']\n",
            "['At iteration 13 the best fitness is 42.567024002582826']\n",
            "['At iteration 14 the best fitness is 40.04214838522254']\n",
            "['At iteration 15 the best fitness is 35.43081883014139']\n",
            "['At iteration 16 the best fitness is 33.606384558632215']\n",
            "['At iteration 17 the best fitness is 31.281358893056805']\n",
            "['At iteration 18 the best fitness is 27.479216040678136']\n",
            "['At iteration 19 the best fitness is 26.29878936521142']\n",
            "['At iteration 20 the best fitness is 26.29878936521142']\n",
            "['At iteration 21 the best fitness is 23.468860215752073']\n",
            "['At iteration 22 the best fitness is 21.998891022326518']\n",
            "['At iteration 23 the best fitness is 21.998891022326518']\n",
            "['At iteration 24 the best fitness is 17.951916174700667']\n",
            "['At iteration 25 the best fitness is 17.951916174700667']\n",
            "['At iteration 26 the best fitness is 17.951916174700667']\n",
            "['At iteration 27 the best fitness is 16.298005487015573']\n",
            "['At iteration 28 the best fitness is 15.581496129976996']\n",
            "['At iteration 29 the best fitness is 14.879778127558774']\n",
            "['At iteration 30 the best fitness is 14.493985212676895']\n",
            "['At iteration 31 the best fitness is 13.749476515838841']\n",
            "['At iteration 32 the best fitness is 13.280230180860746']\n",
            "['At iteration 33 the best fitness is 11.933112162051287']\n",
            "['At iteration 34 the best fitness is 11.933112162051287']\n",
            "['At iteration 35 the best fitness is 11.426613138360969']\n",
            "['At iteration 36 the best fitness is 11.419501133073966']\n",
            "['At iteration 37 the best fitness is 10.499770722443367']\n",
            "['At iteration 38 the best fitness is 10.499770722443367']\n",
            "['At iteration 39 the best fitness is 10.441285315258']\n",
            "['At iteration 40 the best fitness is 10.053032812437605']\n",
            "['At iteration 41 the best fitness is 9.9595143473527']\n",
            "['At iteration 42 the best fitness is 9.648299402958708']\n",
            "['At iteration 43 the best fitness is 9.648299402958708']\n",
            "['At iteration 44 the best fitness is 9.545829390272733']\n",
            "['At iteration 45 the best fitness is 9.545829390272733']\n",
            "['At iteration 46 the best fitness is 9.293119514160242']\n",
            "['At iteration 47 the best fitness is 9.197111703415763']\n",
            "['At iteration 48 the best fitness is 9.07567882241379']\n",
            "['At iteration 49 the best fitness is 8.942258444648465']\n",
            "MVO is optimizing  \"F3\"\n",
            "['At iteration 1 the best fitness is 259581.9989117884']\n",
            "['At iteration 2 the best fitness is 189743.29247198917']\n",
            "['At iteration 3 the best fitness is 171764.84380111564']\n",
            "['At iteration 4 the best fitness is 134361.24551947703']\n",
            "['At iteration 5 the best fitness is 134361.24551947703']\n",
            "['At iteration 6 the best fitness is 120575.82948972262']\n",
            "['At iteration 7 the best fitness is 94689.90545424922']\n",
            "['At iteration 8 the best fitness is 89982.47159624136']\n",
            "['At iteration 9 the best fitness is 89503.35916936627']\n",
            "['At iteration 10 the best fitness is 84542.71900540072']\n",
            "['At iteration 11 the best fitness is 84542.71900540072']\n",
            "['At iteration 12 the best fitness is 74085.5940029773']\n",
            "['At iteration 13 the best fitness is 63930.986738422296']\n",
            "['At iteration 14 the best fitness is 63930.986738422296']\n",
            "['At iteration 15 the best fitness is 63930.986738422296']\n",
            "['At iteration 16 the best fitness is 55741.84409200859']\n",
            "['At iteration 17 the best fitness is 55741.84409200859']\n",
            "['At iteration 18 the best fitness is 48220.7167010356']\n",
            "['At iteration 19 the best fitness is 48220.7167010356']\n",
            "['At iteration 20 the best fitness is 48220.7167010356']\n",
            "['At iteration 21 the best fitness is 46047.7080451523']\n",
            "['At iteration 22 the best fitness is 46047.7080451523']\n",
            "['At iteration 23 the best fitness is 43771.28754313466']\n",
            "['At iteration 24 the best fitness is 41359.85122132741']\n",
            "['At iteration 25 the best fitness is 41359.85122132741']\n",
            "['At iteration 26 the best fitness is 39170.33318555426']\n",
            "['At iteration 27 the best fitness is 36397.310303083614']\n",
            "['At iteration 28 the best fitness is 30968.264835488535']\n",
            "['At iteration 29 the best fitness is 29801.39063888119']\n",
            "['At iteration 30 the best fitness is 28953.422049189256']\n",
            "['At iteration 31 the best fitness is 28645.573057752357']\n",
            "['At iteration 32 the best fitness is 24919.224555394176']\n",
            "['At iteration 33 the best fitness is 24266.611536016884']\n",
            "['At iteration 34 the best fitness is 23348.752988540415']\n",
            "['At iteration 35 the best fitness is 22716.03487933527']\n",
            "['At iteration 36 the best fitness is 22274.74913904238']\n",
            "['At iteration 37 the best fitness is 21711.274958148933']\n",
            "['At iteration 38 the best fitness is 21063.956473820297']\n",
            "['At iteration 39 the best fitness is 19697.337026244277']\n",
            "['At iteration 40 the best fitness is 19548.081652355035']\n",
            "['At iteration 41 the best fitness is 19181.515847423518']\n",
            "['At iteration 42 the best fitness is 18450.80456890821']\n",
            "['At iteration 43 the best fitness is 18019.536935471955']\n",
            "['At iteration 44 the best fitness is 17600.198221429546']\n",
            "['At iteration 45 the best fitness is 17105.927296096717']\n",
            "['At iteration 46 the best fitness is 16893.25324694655']\n",
            "['At iteration 47 the best fitness is 16587.662892278648']\n",
            "['At iteration 48 the best fitness is 16455.626813668976']\n",
            "['At iteration 49 the best fitness is 16389.0927252002']\n",
            "['At iteration 50 the best fitness is 16297.346271734694']\n",
            "MVO is optimizing  \"F3\"\n",
            "['At iteration 1 the best fitness is 168780.00410064077']\n",
            "['At iteration 2 the best fitness is 166091.97780261756']\n",
            "['At iteration 3 the best fitness is 106908.27517706192']\n",
            "['At iteration 4 the best fitness is 106908.27517706192']\n",
            "['At iteration 5 the best fitness is 106908.27517706192']\n",
            "['At iteration 6 the best fitness is 85424.48344957878']\n",
            "['At iteration 7 the best fitness is 85424.48344957878']\n",
            "['At iteration 8 the best fitness is 85424.48344957878']\n",
            "['At iteration 9 the best fitness is 50084.43478665658']\n",
            "['At iteration 10 the best fitness is 47459.993624173']\n",
            "['At iteration 11 the best fitness is 47459.993624173']\n",
            "['At iteration 12 the best fitness is 47459.993624173']\n",
            "['At iteration 13 the best fitness is 47459.993624173']\n",
            "['At iteration 14 the best fitness is 42882.166669782746']\n",
            "['At iteration 15 the best fitness is 42882.166669782746']\n",
            "['At iteration 16 the best fitness is 41350.79796585324']\n",
            "['At iteration 17 the best fitness is 37091.15548904788']\n",
            "['At iteration 18 the best fitness is 36615.30054843547']\n",
            "['At iteration 19 the best fitness is 36615.30054843547']\n",
            "['At iteration 20 the best fitness is 36615.30054843547']\n",
            "['At iteration 21 the best fitness is 31840.118995156332']\n",
            "['At iteration 22 the best fitness is 31840.118995156332']\n",
            "['At iteration 23 the best fitness is 29627.076283590945']\n",
            "['At iteration 24 the best fitness is 28555.23306754188']\n",
            "['At iteration 25 the best fitness is 27077.934138222827']\n",
            "['At iteration 26 the best fitness is 27077.934138222827']\n",
            "['At iteration 27 the best fitness is 27077.934138222827']\n",
            "['At iteration 28 the best fitness is 27077.934138222827']\n",
            "['At iteration 29 the best fitness is 26858.011885558964']\n",
            "['At iteration 30 the best fitness is 24082.27455167971']\n",
            "['At iteration 31 the best fitness is 24082.27455167971']\n",
            "['At iteration 32 the best fitness is 24082.27455167971']\n",
            "['At iteration 33 the best fitness is 23070.162356023266']\n",
            "['At iteration 34 the best fitness is 22035.580209718028']\n",
            "['At iteration 35 the best fitness is 21742.613510951916']\n",
            "['At iteration 36 the best fitness is 21742.613510951916']\n",
            "['At iteration 37 the best fitness is 20579.339196422996']\n",
            "['At iteration 38 the best fitness is 20232.178997018702']\n",
            "['At iteration 39 the best fitness is 19234.717134721974']\n",
            "['At iteration 40 the best fitness is 19179.218069465478']\n",
            "['At iteration 41 the best fitness is 18999.803258727003']\n",
            "['At iteration 42 the best fitness is 18475.04740047334']\n",
            "['At iteration 43 the best fitness is 18215.125734304213']\n",
            "['At iteration 44 the best fitness is 17795.64435609056']\n",
            "['At iteration 45 the best fitness is 17253.859849480505']\n",
            "['At iteration 46 the best fitness is 16864.974759173314']\n",
            "['At iteration 47 the best fitness is 16717.084850617397']\n",
            "['At iteration 48 the best fitness is 16549.24834041853']\n",
            "['At iteration 49 the best fitness is 16449.0687462422']\n",
            "['At iteration 50 the best fitness is 16394.468140632445']\n",
            "MVO is optimizing  \"F3\"\n",
            "['At iteration 1 the best fitness is 136722.86459792758']\n",
            "['At iteration 2 the best fitness is 136722.86459792758']\n",
            "['At iteration 3 the best fitness is 102152.42857898258']\n",
            "['At iteration 4 the best fitness is 96722.88992039289']\n",
            "['At iteration 5 the best fitness is 96722.88992039289']\n",
            "['At iteration 6 the best fitness is 88990.8087545517']\n",
            "['At iteration 7 the best fitness is 76109.03756856736']\n",
            "['At iteration 8 the best fitness is 69783.75213169614']\n",
            "['At iteration 9 the best fitness is 69783.75213169614']\n",
            "['At iteration 10 the best fitness is 62829.87565766655']\n",
            "['At iteration 11 the best fitness is 62829.87565766655']\n",
            "['At iteration 12 the best fitness is 62829.87565766655']\n",
            "['At iteration 13 the best fitness is 59332.46603688406']\n",
            "['At iteration 14 the best fitness is 59320.08148474584']\n",
            "['At iteration 15 the best fitness is 59320.08148474584']\n",
            "['At iteration 16 the best fitness is 55384.90715732627']\n",
            "['At iteration 17 the best fitness is 54741.40998950308']\n",
            "['At iteration 18 the best fitness is 45579.19715999329']\n",
            "['At iteration 19 the best fitness is 45579.19715999329']\n",
            "['At iteration 20 the best fitness is 41466.72595663917']\n",
            "['At iteration 21 the best fitness is 41466.72595663917']\n",
            "['At iteration 22 the best fitness is 40876.828050339325']\n",
            "['At iteration 23 the best fitness is 36936.761382321114']\n",
            "['At iteration 24 the best fitness is 36631.17707175318']\n",
            "['At iteration 25 the best fitness is 33084.80238316026']\n",
            "['At iteration 26 the best fitness is 33084.80238316026']\n",
            "['At iteration 27 the best fitness is 32137.991919589043']\n",
            "['At iteration 28 the best fitness is 29205.109585602688']\n",
            "['At iteration 29 the best fitness is 26024.86119029684']\n",
            "['At iteration 30 the best fitness is 24375.87716258529']\n",
            "['At iteration 31 the best fitness is 24375.87716258529']\n",
            "['At iteration 32 the best fitness is 24214.566765767602']\n",
            "['At iteration 33 the best fitness is 23121.402408577174']\n",
            "['At iteration 34 the best fitness is 22090.48291331383']\n",
            "['At iteration 35 the best fitness is 19793.799501154193']\n",
            "['At iteration 36 the best fitness is 19793.799501154193']\n",
            "['At iteration 37 the best fitness is 19793.799501154193']\n",
            "['At iteration 38 the best fitness is 18723.089081132686']\n",
            "['At iteration 39 the best fitness is 18705.721701581366']\n",
            "['At iteration 40 the best fitness is 17639.41838082153']\n",
            "['At iteration 41 the best fitness is 17561.1014694224']\n",
            "['At iteration 42 the best fitness is 17200.825488470004']\n",
            "['At iteration 43 the best fitness is 16606.894995093837']\n",
            "['At iteration 44 the best fitness is 16464.79701966469']\n",
            "['At iteration 45 the best fitness is 15719.862963022602']\n",
            "['At iteration 46 the best fitness is 15403.421835560737']\n",
            "['At iteration 47 the best fitness is 15271.833741636934']\n",
            "['At iteration 48 the best fitness is 15125.301464796097']\n",
            "['At iteration 49 the best fitness is 14981.563394180459']\n",
            "['At iteration 50 the best fitness is 14920.249477036352']\n",
            "MVO is optimizing  \"F4\"\n",
            "['At iteration 1 the best fitness is 87.20861863325138']\n",
            "['At iteration 2 the best fitness is 87.20861863325138']\n",
            "['At iteration 3 the best fitness is 87.20861863325138']\n",
            "['At iteration 4 the best fitness is 81.16273929586364']\n",
            "['At iteration 5 the best fitness is 81.16273929586364']\n",
            "['At iteration 6 the best fitness is 81.16273929586364']\n",
            "['At iteration 7 the best fitness is 79.35992657094977']\n",
            "['At iteration 8 the best fitness is 79.35992657094977']\n",
            "['At iteration 9 the best fitness is 79.35992657094977']\n",
            "['At iteration 10 the best fitness is 79.35992657094977']\n",
            "['At iteration 11 the best fitness is 79.01085231142557']\n",
            "['At iteration 12 the best fitness is 79.01085231142557']\n",
            "['At iteration 13 the best fitness is 78.59315762641926']\n",
            "['At iteration 14 the best fitness is 73.17142658214618']\n",
            "['At iteration 15 the best fitness is 73.17142658214618']\n",
            "['At iteration 16 the best fitness is 73.17142658214618']\n",
            "['At iteration 17 the best fitness is 72.0929162557938']\n",
            "['At iteration 18 the best fitness is 66.29696142664287']\n",
            "['At iteration 19 the best fitness is 65.67425529649869']\n",
            "['At iteration 20 the best fitness is 65.67425529649869']\n",
            "['At iteration 21 the best fitness is 65.67425529649869']\n",
            "['At iteration 22 the best fitness is 65.32532121992993']\n",
            "['At iteration 23 the best fitness is 63.46439579626941']\n",
            "['At iteration 24 the best fitness is 62.116646917399336']\n",
            "['At iteration 25 the best fitness is 62.05834566816048']\n",
            "['At iteration 26 the best fitness is 59.54109091543342']\n",
            "['At iteration 27 the best fitness is 58.049227192878064']\n",
            "['At iteration 28 the best fitness is 56.897002131935764']\n",
            "['At iteration 29 the best fitness is 56.56818771632674']\n",
            "['At iteration 30 the best fitness is 51.768404511587434']\n",
            "['At iteration 31 the best fitness is 51.768404511587434']\n",
            "['At iteration 32 the best fitness is 50.35991724752147']\n",
            "['At iteration 33 the best fitness is 49.208643816177386']\n",
            "['At iteration 34 the best fitness is 49.208643816177386']\n",
            "['At iteration 35 the best fitness is 48.16282421777979']\n",
            "['At iteration 36 the best fitness is 46.997896144878744']\n",
            "['At iteration 37 the best fitness is 46.62527575876545']\n",
            "['At iteration 38 the best fitness is 44.58562330389393']\n",
            "['At iteration 39 the best fitness is 44.48906534229466']\n",
            "['At iteration 40 the best fitness is 44.48906534229466']\n",
            "['At iteration 41 the best fitness is 43.681293973600944']\n",
            "['At iteration 42 the best fitness is 42.55718396565945']\n",
            "['At iteration 43 the best fitness is 41.763568790540155']\n",
            "['At iteration 44 the best fitness is 40.63504846240703']\n",
            "['At iteration 45 the best fitness is 40.63504846240703']\n",
            "['At iteration 46 the best fitness is 40.297483225801614']\n",
            "['At iteration 47 the best fitness is 39.610340881881385']\n",
            "['At iteration 48 the best fitness is 39.381702028635836']\n",
            "['At iteration 49 the best fitness is 39.16859775419354']\n",
            "['At iteration 50 the best fitness is 38.95542225416756']\n",
            "MVO is optimizing  \"F4\"\n",
            "['At iteration 1 the best fitness is 89.59389563605811']\n",
            "['At iteration 2 the best fitness is 85.93998323060733']\n",
            "['At iteration 3 the best fitness is 85.63183597274981']\n",
            "['At iteration 4 the best fitness is 78.94141410188153']\n",
            "['At iteration 5 the best fitness is 78.27243668946929']\n",
            "['At iteration 6 the best fitness is 78.27243668946929']\n",
            "['At iteration 7 the best fitness is 64.77910019578297']\n",
            "['At iteration 8 the best fitness is 64.77910019578297']\n",
            "['At iteration 9 the best fitness is 64.77910019578297']\n",
            "['At iteration 10 the best fitness is 60.771404388811675']\n",
            "['At iteration 11 the best fitness is 60.771404388811675']\n",
            "['At iteration 12 the best fitness is 56.69520873899443']\n",
            "['At iteration 13 the best fitness is 56.69520873899443']\n",
            "['At iteration 14 the best fitness is 53.48539282414656']\n",
            "['At iteration 15 the best fitness is 53.48539282414656']\n",
            "['At iteration 16 the best fitness is 53.48539282414656']\n",
            "['At iteration 17 the best fitness is 53.48539282414656']\n",
            "['At iteration 18 the best fitness is 53.48539282414656']\n",
            "['At iteration 19 the best fitness is 51.29685795295307']\n",
            "['At iteration 20 the best fitness is 51.29685795295307']\n",
            "['At iteration 21 the best fitness is 49.4692050301757']\n",
            "['At iteration 22 the best fitness is 49.4692050301757']\n",
            "['At iteration 23 the best fitness is 48.097285896471476']\n",
            "['At iteration 24 the best fitness is 47.00824574112333']\n",
            "['At iteration 25 the best fitness is 45.39449743096543']\n",
            "['At iteration 26 the best fitness is 45.39449743096543']\n",
            "['At iteration 27 the best fitness is 44.32138370104849']\n",
            "['At iteration 28 the best fitness is 44.32138370104849']\n",
            "['At iteration 29 the best fitness is 43.75889788875039']\n",
            "['At iteration 30 the best fitness is 41.17928888119294']\n",
            "['At iteration 31 the best fitness is 40.548583188088536']\n",
            "['At iteration 32 the best fitness is 40.548583188088536']\n",
            "['At iteration 33 the best fitness is 39.371664334188566']\n",
            "['At iteration 34 the best fitness is 37.830154765419735']\n",
            "['At iteration 35 the best fitness is 36.33035910487312']\n",
            "['At iteration 36 the best fitness is 36.0017227287819']\n",
            "['At iteration 37 the best fitness is 34.9577011328746']\n",
            "['At iteration 38 the best fitness is 34.465183377670066']\n",
            "['At iteration 39 the best fitness is 33.90247250570789']\n",
            "['At iteration 40 the best fitness is 32.98930095229158']\n",
            "['At iteration 41 the best fitness is 32.429595046253795']\n",
            "['At iteration 42 the best fitness is 30.797845236417025']\n",
            "['At iteration 43 the best fitness is 30.479477780996373']\n",
            "['At iteration 44 the best fitness is 29.754202891388843']\n",
            "['At iteration 45 the best fitness is 29.59586572761085']\n",
            "['At iteration 46 the best fitness is 29.24678958919621']\n",
            "['At iteration 47 the best fitness is 28.412569865938263']\n",
            "['At iteration 48 the best fitness is 28.412569865938263']\n",
            "['At iteration 49 the best fitness is 28.396146094499066']\n",
            "['At iteration 50 the best fitness is 28.265902867162776']\n",
            "MVO is optimizing  \"F4\"\n",
            "['At iteration 1 the best fitness is 90.45287811068596']\n",
            "['At iteration 2 the best fitness is 87.9544010797838']\n",
            "['At iteration 3 the best fitness is 87.9544010797838']\n",
            "['At iteration 4 the best fitness is 87.39348745399968']\n",
            "['At iteration 5 the best fitness is 87.39348745399968']\n",
            "['At iteration 6 the best fitness is 85.53229156365362']\n",
            "['At iteration 7 the best fitness is 78.84286970180837']\n",
            "['At iteration 8 the best fitness is 78.84286970180837']\n",
            "['At iteration 9 the best fitness is 78.63150756599686']\n",
            "['At iteration 10 the best fitness is 77.25950824050126']\n",
            "['At iteration 11 the best fitness is 71.35199864831017']\n",
            "['At iteration 12 the best fitness is 69.65638227158347']\n",
            "['At iteration 13 the best fitness is 69.54011473111358']\n",
            "['At iteration 14 the best fitness is 69.54011473111358']\n",
            "['At iteration 15 the best fitness is 69.17594546318979']\n",
            "['At iteration 16 the best fitness is 68.21116900200464']\n",
            "['At iteration 17 the best fitness is 68.21116900200464']\n",
            "['At iteration 18 the best fitness is 66.19132782107945']\n",
            "['At iteration 19 the best fitness is 64.73949147320187']\n",
            "['At iteration 20 the best fitness is 59.98023570084251']\n",
            "['At iteration 21 the best fitness is 59.98023570084251']\n",
            "['At iteration 22 the best fitness is 55.790684899877434']\n",
            "['At iteration 23 the best fitness is 55.790684899877434']\n",
            "['At iteration 24 the best fitness is 52.766024530260225']\n",
            "['At iteration 25 the best fitness is 51.23850476295756']\n",
            "['At iteration 26 the best fitness is 51.23850476295756']\n",
            "['At iteration 27 the best fitness is 51.23850476295756']\n",
            "['At iteration 28 the best fitness is 50.64009978682535']\n",
            "['At iteration 29 the best fitness is 50.64009978682535']\n",
            "['At iteration 30 the best fitness is 50.64009978682535']\n",
            "['At iteration 31 the best fitness is 49.924048089295496']\n",
            "['At iteration 32 the best fitness is 49.924048089295496']\n",
            "['At iteration 33 the best fitness is 48.33511984465692']\n",
            "['At iteration 34 the best fitness is 48.33511984465692']\n",
            "['At iteration 35 the best fitness is 47.57085146157108']\n",
            "['At iteration 36 the best fitness is 47.10431937007601']\n",
            "['At iteration 37 the best fitness is 45.822959822539474']\n",
            "['At iteration 38 the best fitness is 45.822959822539474']\n",
            "['At iteration 39 the best fitness is 44.14845052965605']\n",
            "['At iteration 40 the best fitness is 44.14845052965605']\n",
            "['At iteration 41 the best fitness is 43.3377702442485']\n",
            "['At iteration 42 the best fitness is 42.615038629430245']\n",
            "['At iteration 43 the best fitness is 42.615038629430245']\n",
            "['At iteration 44 the best fitness is 41.66606109294556']\n",
            "['At iteration 45 the best fitness is 41.66606109294556']\n",
            "['At iteration 46 the best fitness is 41.222263999767236']\n",
            "['At iteration 47 the best fitness is 40.507301955369385']\n",
            "['At iteration 48 the best fitness is 40.39389397779128']\n",
            "['At iteration 49 the best fitness is 39.876921286193976']\n",
            "['At iteration 50 the best fitness is 39.78231179518983']\n",
            "WOA is optimizing  \"F3\"\n",
            "['At iteration 0 the best fitness is 145612.06888663652']\n",
            "['At iteration 1 the best fitness is 143686.8659815473']\n",
            "['At iteration 2 the best fitness is 100162.01299453274']\n",
            "['At iteration 3 the best fitness is 100162.01299453274']\n",
            "['At iteration 4 the best fitness is 87865.81660042489']\n",
            "['At iteration 5 the best fitness is 87865.81660042489']\n",
            "['At iteration 6 the best fitness is 87865.81660042489']\n",
            "['At iteration 7 the best fitness is 87865.81660042489']\n",
            "['At iteration 8 the best fitness is 87865.81660042489']\n",
            "['At iteration 9 the best fitness is 84055.35214469695']\n",
            "['At iteration 10 the best fitness is 83944.66704476564']\n",
            "['At iteration 11 the best fitness is 83727.88271359811']\n",
            "['At iteration 12 the best fitness is 83727.88271359811']\n",
            "['At iteration 13 the best fitness is 83418.66177290303']\n",
            "['At iteration 14 the best fitness is 82992.09977392908']\n",
            "['At iteration 15 the best fitness is 82992.09977392908']\n",
            "['At iteration 16 the best fitness is 82928.78043460975']\n",
            "['At iteration 17 the best fitness is 82921.37128982613']\n",
            "['At iteration 18 the best fitness is 82654.54098875752']\n",
            "['At iteration 19 the best fitness is 82057.78667522587']\n",
            "['At iteration 20 the best fitness is 81895.20952585213']\n",
            "['At iteration 21 the best fitness is 80774.08219776615']\n",
            "['At iteration 22 the best fitness is 80774.08219776615']\n",
            "['At iteration 23 the best fitness is 80774.08219776615']\n",
            "['At iteration 24 the best fitness is 80616.71763946854']\n",
            "['At iteration 25 the best fitness is 80616.71763946854']\n",
            "['At iteration 26 the best fitness is 80616.71763946854']\n",
            "['At iteration 27 the best fitness is 80363.37771862635']\n",
            "['At iteration 28 the best fitness is 80363.37771862635']\n",
            "['At iteration 29 the best fitness is 78888.44587674318']\n",
            "['At iteration 30 the best fitness is 78874.02562333275']\n",
            "['At iteration 31 the best fitness is 78874.02562333275']\n",
            "['At iteration 32 the best fitness is 78842.98267264216']\n",
            "['At iteration 33 the best fitness is 78265.71865228856']\n",
            "['At iteration 34 the best fitness is 78178.49179545906']\n",
            "['At iteration 35 the best fitness is 77966.5494630753']\n",
            "['At iteration 36 the best fitness is 77902.94687792979']\n",
            "['At iteration 37 the best fitness is 77902.94687792979']\n",
            "['At iteration 38 the best fitness is 77902.94687792979']\n",
            "['At iteration 39 the best fitness is 77734.19467482048']\n",
            "['At iteration 40 the best fitness is 77730.06987416508']\n",
            "['At iteration 41 the best fitness is 77246.94124481028']\n",
            "['At iteration 42 the best fitness is 76783.17235688526']\n",
            "['At iteration 43 the best fitness is 76766.82823257531']\n",
            "['At iteration 44 the best fitness is 76567.53729481203']\n",
            "['At iteration 45 the best fitness is 76526.71640846146']\n",
            "['At iteration 46 the best fitness is 76526.71640846146']\n",
            "['At iteration 47 the best fitness is 76520.72145735178']\n",
            "['At iteration 48 the best fitness is 76403.85928476718']\n",
            "['At iteration 49 the best fitness is 76111.298046584']\n",
            "WOA is optimizing  \"F3\"\n",
            "['At iteration 0 the best fitness is 123779.21136378004']\n",
            "['At iteration 1 the best fitness is 123779.21136378004']\n",
            "['At iteration 2 the best fitness is 104911.74048614313']\n",
            "['At iteration 3 the best fitness is 104911.74048614313']\n",
            "['At iteration 4 the best fitness is 103241.70463105026']\n",
            "['At iteration 5 the best fitness is 100679.38317574591']\n",
            "['At iteration 6 the best fitness is 100618.24627412221']\n",
            "['At iteration 7 the best fitness is 100590.92577967157']\n",
            "['At iteration 8 the best fitness is 100503.89667524902']\n",
            "['At iteration 9 the best fitness is 100432.54298786477']\n",
            "['At iteration 10 the best fitness is 100432.54298786477']\n",
            "['At iteration 11 the best fitness is 100432.54298786477']\n",
            "['At iteration 12 the best fitness is 100432.54298786477']\n",
            "['At iteration 13 the best fitness is 100432.54298786477']\n",
            "['At iteration 14 the best fitness is 100432.54298786477']\n",
            "['At iteration 15 the best fitness is 100432.54298786477']\n",
            "['At iteration 16 the best fitness is 100432.54298786477']\n",
            "['At iteration 17 the best fitness is 100432.54298786477']\n",
            "['At iteration 18 the best fitness is 100317.26569418106']\n",
            "['At iteration 19 the best fitness is 100317.26569418106']\n",
            "['At iteration 20 the best fitness is 100317.26569418106']\n",
            "['At iteration 21 the best fitness is 100104.08022276021']\n",
            "['At iteration 22 the best fitness is 98401.16403976416']\n",
            "['At iteration 23 the best fitness is 98401.16403976416']\n",
            "['At iteration 24 the best fitness is 98381.77547680045']\n",
            "['At iteration 25 the best fitness is 98130.90744514973']\n",
            "['At iteration 26 the best fitness is 98130.90744514973']\n",
            "['At iteration 27 the best fitness is 98106.41087207194']\n",
            "['At iteration 28 the best fitness is 98106.41087207194']\n",
            "['At iteration 29 the best fitness is 97987.80910784006']\n",
            "['At iteration 30 the best fitness is 97949.32672509334']\n",
            "['At iteration 31 the best fitness is 97938.46775166706']\n",
            "['At iteration 32 the best fitness is 97938.46775166706']\n",
            "['At iteration 33 the best fitness is 97935.98365015979']\n",
            "['At iteration 34 the best fitness is 97927.2228921947']\n",
            "['At iteration 35 the best fitness is 97926.8004232505']\n",
            "['At iteration 36 the best fitness is 97751.55359232062']\n",
            "['At iteration 37 the best fitness is 97712.75282131591']\n",
            "['At iteration 38 the best fitness is 97711.66607957378']\n",
            "['At iteration 39 the best fitness is 97697.28434427526']\n",
            "['At iteration 40 the best fitness is 97697.28434427526']\n",
            "['At iteration 41 the best fitness is 97692.05785658973']\n",
            "['At iteration 42 the best fitness is 97692.05785658973']\n",
            "['At iteration 43 the best fitness is 97692.05785658973']\n",
            "['At iteration 44 the best fitness is 97677.74403881018']\n",
            "['At iteration 45 the best fitness is 97574.95637644695']\n",
            "['At iteration 46 the best fitness is 97550.86978074629']\n",
            "['At iteration 47 the best fitness is 97463.67523732746']\n",
            "['At iteration 48 the best fitness is 97447.42113659935']\n",
            "['At iteration 49 the best fitness is 97420.54209062622']\n",
            "WOA is optimizing  \"F3\"\n",
            "['At iteration 0 the best fitness is 136285.8406633009']\n",
            "['At iteration 1 the best fitness is 115543.6242762229']\n",
            "['At iteration 2 the best fitness is 115543.6242762229']\n",
            "['At iteration 3 the best fitness is 115357.57434870074']\n",
            "['At iteration 4 the best fitness is 115357.57434870074']\n",
            "['At iteration 5 the best fitness is 115357.57434870074']\n",
            "['At iteration 6 the best fitness is 115333.45815245154']\n",
            "['At iteration 7 the best fitness is 115333.45815245154']\n",
            "['At iteration 8 the best fitness is 115333.45815245154']\n",
            "['At iteration 9 the best fitness is 115333.45815245154']\n",
            "['At iteration 10 the best fitness is 115333.45815245154']\n",
            "['At iteration 11 the best fitness is 114716.32985900027']\n",
            "['At iteration 12 the best fitness is 114716.32985900027']\n",
            "['At iteration 13 the best fitness is 114470.08821683849']\n",
            "['At iteration 14 the best fitness is 114470.08821683849']\n",
            "['At iteration 15 the best fitness is 113855.78662840142']\n",
            "['At iteration 16 the best fitness is 112918.11483493348']\n",
            "['At iteration 17 the best fitness is 112918.11483493348']\n",
            "['At iteration 18 the best fitness is 112918.11483493348']\n",
            "['At iteration 19 the best fitness is 112918.11483493348']\n",
            "['At iteration 20 the best fitness is 111650.95285398485']\n",
            "['At iteration 21 the best fitness is 111557.97136097061']\n",
            "['At iteration 22 the best fitness is 111557.97136097061']\n",
            "['At iteration 23 the best fitness is 111377.56974876719']\n",
            "['At iteration 24 the best fitness is 110982.06155293863']\n",
            "['At iteration 25 the best fitness is 110936.34695190561']\n",
            "['At iteration 26 the best fitness is 110931.15499515331']\n",
            "['At iteration 27 the best fitness is 110931.15499515331']\n",
            "['At iteration 28 the best fitness is 110931.15499515331']\n",
            "['At iteration 29 the best fitness is 110931.15499515331']\n",
            "['At iteration 30 the best fitness is 110855.72835765526']\n",
            "['At iteration 31 the best fitness is 110855.72835765526']\n",
            "['At iteration 32 the best fitness is 110855.72835765526']\n",
            "['At iteration 33 the best fitness is 110553.58404725022']\n",
            "['At iteration 34 the best fitness is 110553.58404725022']\n",
            "['At iteration 35 the best fitness is 110202.66906927069']\n",
            "['At iteration 36 the best fitness is 109138.38603631481']\n",
            "['At iteration 37 the best fitness is 105688.66267389094']\n",
            "['At iteration 38 the best fitness is 105600.21287486247']\n",
            "['At iteration 39 the best fitness is 105568.97274642026']\n",
            "['At iteration 40 the best fitness is 105339.6385074076']\n",
            "['At iteration 41 the best fitness is 104962.18193518813']\n",
            "['At iteration 42 the best fitness is 104780.61513859977']\n",
            "['At iteration 43 the best fitness is 104123.8910067344']\n",
            "['At iteration 44 the best fitness is 103648.1100506247']\n",
            "['At iteration 45 the best fitness is 103592.94674696904']\n",
            "['At iteration 46 the best fitness is 102705.5087395004']\n",
            "['At iteration 47 the best fitness is 102531.96271082436']\n",
            "['At iteration 48 the best fitness is 102426.27704510407']\n",
            "['At iteration 49 the best fitness is 102264.47563715222']\n",
            "WOA is optimizing  \"F4\"\n",
            "['At iteration 0 the best fitness is 86.48482779651611']\n",
            "['At iteration 1 the best fitness is 85.56384550034358']\n",
            "['At iteration 2 the best fitness is 78.84771309416557']\n",
            "['At iteration 3 the best fitness is 78.61918826830184']\n",
            "['At iteration 4 the best fitness is 78.1751493118888']\n",
            "['At iteration 5 the best fitness is 75.91517995321149']\n",
            "['At iteration 6 the best fitness is 75.91105326120325']\n",
            "['At iteration 7 the best fitness is 75.90010740410618']\n",
            "['At iteration 8 the best fitness is 75.90010740410618']\n",
            "['At iteration 9 the best fitness is 75.90010740410618']\n",
            "['At iteration 10 the best fitness is 75.90010740410618']\n",
            "['At iteration 11 the best fitness is 75.90010740410618']\n",
            "['At iteration 12 the best fitness is 75.90010740410618']\n",
            "['At iteration 13 the best fitness is 75.90010740410618']\n",
            "['At iteration 14 the best fitness is 75.89524328490785']\n",
            "['At iteration 15 the best fitness is 75.89524328490785']\n",
            "['At iteration 16 the best fitness is 75.89524328490785']\n",
            "['At iteration 17 the best fitness is 75.89524328490785']\n",
            "['At iteration 18 the best fitness is 75.89524328490785']\n",
            "['At iteration 19 the best fitness is 75.8884371389759']\n",
            "['At iteration 20 the best fitness is 75.8884371389759']\n",
            "['At iteration 21 the best fitness is 75.88491937282784']\n",
            "['At iteration 22 the best fitness is 75.88491937282784']\n",
            "['At iteration 23 the best fitness is 75.87934027346728']\n",
            "['At iteration 24 the best fitness is 75.87934027346728']\n",
            "['At iteration 25 the best fitness is 75.87934027346728']\n",
            "['At iteration 26 the best fitness is 75.87934027346728']\n",
            "['At iteration 27 the best fitness is 75.87749189392474']\n",
            "['At iteration 28 the best fitness is 75.87749189392474']\n",
            "['At iteration 29 the best fitness is 75.86330423711685']\n",
            "['At iteration 30 the best fitness is 75.86330423711685']\n",
            "['At iteration 31 the best fitness is 75.85164083079755']\n",
            "['At iteration 32 the best fitness is 75.85119474278297']\n",
            "['At iteration 33 the best fitness is 75.85040843944685']\n",
            "['At iteration 34 the best fitness is 75.85040843944685']\n",
            "['At iteration 35 the best fitness is 75.85040843944685']\n",
            "['At iteration 36 the best fitness is 75.85040843944685']\n",
            "['At iteration 37 the best fitness is 75.85040843944685']\n",
            "['At iteration 38 the best fitness is 75.85040843944685']\n",
            "['At iteration 39 the best fitness is 75.85040843944685']\n",
            "['At iteration 40 the best fitness is 75.85040843944685']\n",
            "['At iteration 41 the best fitness is 75.85040843944685']\n",
            "['At iteration 42 the best fitness is 75.85040843944685']\n",
            "['At iteration 43 the best fitness is 75.85040843944685']\n",
            "['At iteration 44 the best fitness is 75.85040843944685']\n",
            "['At iteration 45 the best fitness is 75.85040843944685']\n",
            "['At iteration 46 the best fitness is 75.85040843944685']\n",
            "['At iteration 47 the best fitness is 75.85005302568334']\n",
            "['At iteration 48 the best fitness is 75.85005302568334']\n",
            "['At iteration 49 the best fitness is 75.85002861986248']\n",
            "WOA is optimizing  \"F4\"\n",
            "['At iteration 0 the best fitness is 92.92676865036233']\n",
            "['At iteration 1 the best fitness is 92.92676865036233']\n",
            "['At iteration 2 the best fitness is 88.77396633517671']\n",
            "['At iteration 3 the best fitness is 79.30403975360741']\n",
            "['At iteration 4 the best fitness is 79.14621614407082']\n",
            "['At iteration 5 the best fitness is 79.14621614407082']\n",
            "['At iteration 6 the best fitness is 79.14621614407082']\n",
            "['At iteration 7 the best fitness is 78.57462409427656']\n",
            "['At iteration 8 the best fitness is 77.75598848304182']\n",
            "['At iteration 9 the best fitness is 77.42029590170205']\n",
            "['At iteration 10 the best fitness is 77.36730707870996']\n",
            "['At iteration 11 the best fitness is 77.35191603973024']\n",
            "['At iteration 12 the best fitness is 77.35191603973024']\n",
            "['At iteration 13 the best fitness is 77.35191603973024']\n",
            "['At iteration 14 the best fitness is 77.35191603973024']\n",
            "['At iteration 15 the best fitness is 77.32494328592658']\n",
            "['At iteration 16 the best fitness is 77.11537920750075']\n",
            "['At iteration 17 the best fitness is 70.22077731101045']\n",
            "['At iteration 18 the best fitness is 66.17642133562852']\n",
            "['At iteration 19 the best fitness is 65.48435656285727']\n",
            "['At iteration 20 the best fitness is 63.33117809868616']\n",
            "['At iteration 21 the best fitness is 63.1230518058317']\n",
            "['At iteration 22 the best fitness is 63.10450368576193']\n",
            "['At iteration 23 the best fitness is 63.10450368576193']\n",
            "['At iteration 24 the best fitness is 63.079632217653966']\n",
            "['At iteration 25 the best fitness is 63.07648225523446']\n",
            "['At iteration 26 the best fitness is 62.952400747732675']\n",
            "['At iteration 27 the best fitness is 62.925057743153225']\n",
            "['At iteration 28 the best fitness is 62.925057743153225']\n",
            "['At iteration 29 the best fitness is 62.901141122244894']\n",
            "['At iteration 30 the best fitness is 62.885821210903984']\n",
            "['At iteration 31 the best fitness is 62.76205870730966']\n",
            "['At iteration 32 the best fitness is 62.76205870730966']\n",
            "['At iteration 33 the best fitness is 62.74192714404545']\n",
            "['At iteration 34 the best fitness is 62.62567405393949']\n",
            "['At iteration 35 the best fitness is 62.618752062420334']\n",
            "['At iteration 36 the best fitness is 62.618752062420334']\n",
            "['At iteration 37 the best fitness is 62.592870400031565']\n",
            "['At iteration 38 the best fitness is 62.592870400031565']\n",
            "['At iteration 39 the best fitness is 62.571206680508034']\n",
            "['At iteration 40 the best fitness is 62.557730464550595']\n",
            "['At iteration 41 the best fitness is 62.557730464550595']\n",
            "['At iteration 42 the best fitness is 62.5498767513972']\n",
            "['At iteration 43 the best fitness is 62.5498767513972']\n",
            "['At iteration 44 the best fitness is 62.5459863986808']\n",
            "['At iteration 45 the best fitness is 62.54259340667298']\n",
            "['At iteration 46 the best fitness is 62.54259340667298']\n",
            "['At iteration 47 the best fitness is 62.535791225884324']\n",
            "['At iteration 48 the best fitness is 62.535791225884324']\n",
            "['At iteration 49 the best fitness is 62.53508667103315']\n",
            "WOA is optimizing  \"F4\"\n",
            "['At iteration 0 the best fitness is 90.62378343251032']\n",
            "['At iteration 1 the best fitness is 90.62378343251032']\n",
            "['At iteration 2 the best fitness is 82.87323674016372']\n",
            "['At iteration 3 the best fitness is 80.3650283822518']\n",
            "['At iteration 4 the best fitness is 76.97449133340338']\n",
            "['At iteration 5 the best fitness is 75.79717132968459']\n",
            "['At iteration 6 the best fitness is 75.79717132968459']\n",
            "['At iteration 7 the best fitness is 75.77123311141236']\n",
            "['At iteration 8 the best fitness is 75.66438272359137']\n",
            "['At iteration 9 the best fitness is 75.66438272359137']\n",
            "['At iteration 10 the best fitness is 75.66438272359137']\n",
            "['At iteration 11 the best fitness is 75.66438272359137']\n",
            "['At iteration 12 the best fitness is 75.66438272359137']\n",
            "['At iteration 13 the best fitness is 75.6570491648774']\n",
            "['At iteration 14 the best fitness is 75.6570491648774']\n",
            "['At iteration 15 the best fitness is 75.6570491648774']\n",
            "['At iteration 16 the best fitness is 75.6570491648774']\n",
            "['At iteration 17 the best fitness is 75.6570491648774']\n",
            "['At iteration 18 the best fitness is 75.63749829227821']\n",
            "['At iteration 19 the best fitness is 75.6340763812352']\n",
            "['At iteration 20 the best fitness is 75.62912974354455']\n",
            "['At iteration 21 the best fitness is 75.62912974354455']\n",
            "['At iteration 22 the best fitness is 75.62912974354455']\n",
            "['At iteration 23 the best fitness is 75.62912974354455']\n",
            "['At iteration 24 the best fitness is 75.62912974354455']\n",
            "['At iteration 25 the best fitness is 75.62912974354455']\n",
            "['At iteration 26 the best fitness is 75.62912974354455']\n",
            "['At iteration 27 the best fitness is 75.62907005409703']\n",
            "['At iteration 28 the best fitness is 75.62907005409703']\n",
            "['At iteration 29 the best fitness is 75.62907005409703']\n",
            "['At iteration 30 the best fitness is 75.62907005409703']\n",
            "['At iteration 31 the best fitness is 75.62907005409703']\n",
            "['At iteration 32 the best fitness is 75.62907005409703']\n",
            "['At iteration 33 the best fitness is 75.62907005409703']\n",
            "['At iteration 34 the best fitness is 75.6268556833601']\n",
            "['At iteration 35 the best fitness is 75.6268556833601']\n",
            "['At iteration 36 the best fitness is 75.6268556833601']\n",
            "['At iteration 37 the best fitness is 75.6268556833601']\n",
            "['At iteration 38 the best fitness is 75.62665325954376']\n",
            "['At iteration 39 the best fitness is 75.62665325954376']\n",
            "['At iteration 40 the best fitness is 75.62657559699768']\n",
            "['At iteration 41 the best fitness is 75.62657559699768']\n",
            "['At iteration 42 the best fitness is 75.62657559699768']\n",
            "['At iteration 43 the best fitness is 75.62652364217196']\n",
            "['At iteration 44 the best fitness is 75.62652364217196']\n",
            "['At iteration 45 the best fitness is 75.62652364217196']\n",
            "['At iteration 46 the best fitness is 75.62652364217196']\n",
            "['At iteration 47 the best fitness is 75.62652364217196']\n",
            "['At iteration 48 the best fitness is 75.62652364217196']\n",
            "['At iteration 49 the best fitness is 75.62652364217196']\n",
            "Execution completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1mB9AVhQuwbH"
      },
      "source": [
        "<h2>Results Files and Plots</h2>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "642Oh7QUpRhL"
      },
      "source": [
        "#import some useful packages to view the results' files in colab\n",
        "import pandas as pd\n",
        "from IPython.display import Image\n",
        "import os\n",
        "import datetime\n",
        "import ipywidgets as widgets"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UOLHaWwBRf7D",
        "outputId": "f8f50a36-de57-4b3c-a7d7-d8cf21ef2c64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "3b28b90d69084199b50f0df0c1e92741",
            "9554067eb8014f6f9cd0c0e4d30165f9",
            "02fdbef9609840cbbcb7796da6fc93c5"
          ]
        }
      },
      "source": [
        "#Select the experiments folder\n",
        "foldernames = [filename for filename in os.listdir() if filename.startswith(str(datetime.datetime.now().year))]\n",
        "drop_folder = widgets.Dropdown(options=foldernames, description='Select folder:')\n",
        "drop_folder"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dropdown(description='Select folder:', options=('2024-05-26-18-37-02',), value='2024-05-26-18-37-02')"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3b28b90d69084199b50f0df0c1e92741"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xm8UuPRA6uIb"
      },
      "source": [
        "#Get the selected folder\n",
        "foldername = drop_folder.value"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qjNFP9NqvNV3"
      },
      "source": [
        "<h4>Average Results File</h4>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U_PNBYEYp2cn",
        "outputId": "f2271c1f-776b-4703-a4bf-a47df6413d7e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "#Show the average results file\n",
        "filename = foldername +'/experiment.csv'\n",
        "df = pd.read_csv(filename)\n",
        "df.head(4)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Optimizer objfname  ExecutionTime      Iter1      Iter2      Iter3  \\\n",
              "0        GA       F3           0.57  168679.16  159867.15  159867.15   \n",
              "1        GA       F4           0.21      90.52      87.83      86.92   \n",
              "2       PSO       F3           1.38   91807.84   91807.84   91807.84   \n",
              "3       PSO       F4           1.20      91.01      87.91      82.78   \n",
              "\n",
              "       Iter4      Iter5      Iter6      Iter7  ...    Iter41    Iter42  \\\n",
              "0  159867.15  159867.15  159867.15  158602.35  ...  84119.66  84119.66   \n",
              "1      86.87      86.87      86.87      86.87  ...     79.28     79.28   \n",
              "2   79086.12   68047.80   57229.97   51189.48  ...   5003.77   4818.22   \n",
              "3      77.08      71.46      65.75      60.03  ...     10.04      9.97   \n",
              "\n",
              "     Iter43    Iter44    Iter45    Iter46    Iter47    Iter48    Iter49  \\\n",
              "0  84119.66  84119.66  84119.66  84119.66  84119.66  84119.66  84119.66   \n",
              "1     79.28     77.44     77.44     77.44     77.44     77.44     77.44   \n",
              "2   4686.43   4618.77   4541.59   4421.66   4283.27   4260.41   4126.81   \n",
              "3      9.88      9.71      9.50      9.40      9.35      9.34      9.17   \n",
              "\n",
              "     Iter50  \n",
              "0  84119.66  \n",
              "1     77.44  \n",
              "2   4057.94  \n",
              "3      9.15  \n",
              "\n",
              "[4 rows x 53 columns]"
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              "      <th></th>\n",
              "      <th>Optimizer</th>\n",
              "      <th>objfname</th>\n",
              "      <th>ExecutionTime</th>\n",
              "      <th>Iter1</th>\n",
              "      <th>Iter2</th>\n",
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              "      <th>Iter44</th>\n",
              "      <th>Iter45</th>\n",
              "      <th>Iter46</th>\n",
              "      <th>Iter47</th>\n",
              "      <th>Iter48</th>\n",
              "      <th>Iter49</th>\n",
              "      <th>Iter50</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GA</td>\n",
              "      <td>F3</td>\n",
              "      <td>0.57</td>\n",
              "      <td>168679.16</td>\n",
              "      <td>159867.15</td>\n",
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              "      <td>159867.15</td>\n",
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              "      <td>159867.15</td>\n",
              "      <td>158602.35</td>\n",
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              "      <td>84119.66</td>\n",
              "      <td>84119.66</td>\n",
              "      <td>84119.66</td>\n",
              "      <td>84119.66</td>\n",
              "      <td>84119.66</td>\n",
              "      <td>84119.66</td>\n",
              "      <td>84119.66</td>\n",
              "      <td>84119.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GA</td>\n",
              "      <td>F4</td>\n",
              "      <td>0.21</td>\n",
              "      <td>90.52</td>\n",
              "      <td>87.83</td>\n",
              "      <td>86.92</td>\n",
              "      <td>86.87</td>\n",
              "      <td>86.87</td>\n",
              "      <td>86.87</td>\n",
              "      <td>86.87</td>\n",
              "      <td>...</td>\n",
              "      <td>79.28</td>\n",
              "      <td>79.28</td>\n",
              "      <td>79.28</td>\n",
              "      <td>77.44</td>\n",
              "      <td>77.44</td>\n",
              "      <td>77.44</td>\n",
              "      <td>77.44</td>\n",
              "      <td>77.44</td>\n",
              "      <td>77.44</td>\n",
              "      <td>77.44</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>PSO</td>\n",
              "      <td>F3</td>\n",
              "      <td>1.38</td>\n",
              "      <td>91807.84</td>\n",
              "      <td>91807.84</td>\n",
              "      <td>91807.84</td>\n",
              "      <td>79086.12</td>\n",
              "      <td>68047.80</td>\n",
              "      <td>57229.97</td>\n",
              "      <td>51189.48</td>\n",
              "      <td>...</td>\n",
              "      <td>5003.77</td>\n",
              "      <td>4818.22</td>\n",
              "      <td>4686.43</td>\n",
              "      <td>4618.77</td>\n",
              "      <td>4541.59</td>\n",
              "      <td>4421.66</td>\n",
              "      <td>4283.27</td>\n",
              "      <td>4260.41</td>\n",
              "      <td>4126.81</td>\n",
              "      <td>4057.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>PSO</td>\n",
              "      <td>F4</td>\n",
              "      <td>1.20</td>\n",
              "      <td>91.01</td>\n",
              "      <td>87.91</td>\n",
              "      <td>82.78</td>\n",
              "      <td>77.08</td>\n",
              "      <td>71.46</td>\n",
              "      <td>65.75</td>\n",
              "      <td>60.03</td>\n",
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              "      <td>10.04</td>\n",
              "      <td>9.97</td>\n",
              "      <td>9.88</td>\n",
              "      <td>9.71</td>\n",
              "      <td>9.50</td>\n",
              "      <td>9.40</td>\n",
              "      <td>9.35</td>\n",
              "      <td>9.34</td>\n",
              "      <td>9.17</td>\n",
              "      <td>9.15</td>\n",
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              "      buttonEl.style.display =\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-036f2718-0fc9-4c15-9fb1-118223c5fa76');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "            style=\"display:none;\">\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    60% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2ba36777-1bfb-4405-aa82-dd532d9cc5b4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sXSfvROovXRc"
      },
      "source": [
        "<h4>Detailed Results File</h4>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RXU4upcgqcX_",
        "outputId": "edb0cbe0-e545-4a45-fa91-2fa4f0ed3c4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#Show the detailed results file\n",
        "filename = foldername +'/experiment_details.csv'\n",
        "df = pd.read_csv(filename)\n",
        "df.head(12)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Optimizer objfname  ExecutionTime  \\\n",
              "0         GA       F3       0.521535   \n",
              "1         GA       F3       0.615502   \n",
              "2         GA       F3       0.569868   \n",
              "3         GA       F4       0.200179   \n",
              "4         GA       F4       0.251925   \n",
              "5         GA       F4       0.190003   \n",
              "6        PSO       F3       1.320900   \n",
              "7        PSO       F3       1.407495   \n",
              "8        PSO       F3       1.397210   \n",
              "9        PSO       F4       0.859286   \n",
              "10       PSO       F4       1.126943   \n",
              "11       PSO       F4       1.608603   \n",
              "\n",
              "                                           Individual          Iter1  \\\n",
              "0   [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ...  130570.166419   \n",
              "1   [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ...  206974.088239   \n",
              "2   [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ...  168493.213187   \n",
              "3   [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ...      91.493117   \n",
              "4   [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ...      89.737736   \n",
              "5   [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ...      90.316431   \n",
              "6   [ 11.97052812 -22.16233074  10.2017005   10.62...  100570.393233   \n",
              "7   [ -8.10768215   5.58927723  14.1890242  -35.20...   97804.329254   \n",
              "8   [  0.17904748  -8.36584397  29.24199282 -32.90...   77048.803686   \n",
              "9   [ 0.11551122 -1.17811379 -5.95366504 -1.711534...      92.688420   \n",
              "10  [-9.32644819  6.49707997 -5.61186936 -5.427358...      89.535814   \n",
              "11  [  9.34763946   2.46966565 -10.42404437  10.40...      90.793971   \n",
              "\n",
              "            Iter2          Iter3          Iter4          Iter5          Iter6  \\\n",
              "0   130570.166419  130570.166419  130570.166419  130570.166419  130570.166419   \n",
              "1   180538.073053  180538.073053  180538.073053  180538.073053  180538.073053   \n",
              "2   168493.213187  168493.213187  168493.213187  168493.213187  168493.213187   \n",
              "3       86.644102      86.644102      86.644102      86.644102      86.644102   \n",
              "4       89.737736      87.019513      87.019513      87.019513      87.019513   \n",
              "5       87.100211      87.100211      86.933688      86.933688      86.933688   \n",
              "6   100570.393233  100570.393233   78863.766888   60687.383599   50019.614952   \n",
              "7    97804.329254   97804.329254   97804.329254   88667.652076   66881.938917   \n",
              "8    77048.803686   77048.803686   60590.258165   54788.367680   54788.367680   \n",
              "9       87.098402      81.949001      76.362962      70.717001      65.147688   \n",
              "10      89.099842      83.390472      77.535814      71.940283      66.158472   \n",
              "11      87.535239      82.986821      77.353088      71.720253      65.956182   \n",
              "\n",
              "    ...        Iter41        Iter42        Iter43        Iter44        Iter45  \\\n",
              "0   ...  81259.692894  81259.692894  81259.692894  81259.692894  81259.692894   \n",
              "1   ...  96840.601881  96840.601881  96840.601881  96840.601881  96840.601881   \n",
              "2   ...  74258.692160  74258.692160  74258.692160  74258.692160  74258.692160   \n",
              "3   ...     83.368319     83.368319     83.368319     83.368319     83.368319   \n",
              "4   ...     81.337952     81.337952     81.337952     81.337952     81.337952   \n",
              "5   ...     73.134760     73.134760     73.134760     67.604958     67.604958   \n",
              "6   ...   5310.821403   5068.236369   4962.480617   4900.331305   4859.780326   \n",
              "7   ...   4547.411869   4442.291083   4159.896201   4159.896201   4051.697568   \n",
              "8   ...   5153.081050   4944.133431   4936.921069   4796.089922   4713.281683   \n",
              "9   ...      7.064083      7.064083      6.965760      6.965760      6.965760   \n",
              "10  ...     11.516998     11.351656     11.263149     10.771106     10.244018   \n",
              "11  ...     11.532958     11.489916     11.406231     11.403637     11.276678   \n",
              "\n",
              "          Iter46        Iter47        Iter48        Iter49        Iter50  \n",
              "0   81259.692894  81259.692894  81259.692894  81259.692894  81259.692894  \n",
              "1   96840.601881  96840.601881  96840.601881  96840.601881  96840.601881  \n",
              "2   74258.692160  74258.692160  74258.692160  74258.692160  74258.692160  \n",
              "3      83.368319     83.368319     83.368319     83.368319     83.368319  \n",
              "4      81.337952     81.337952     81.337952     81.337952     81.337952  \n",
              "5      67.604958     67.604958     67.604958     67.604958     67.604958  \n",
              "6    4756.901542   4542.767308   4542.767308   4384.864903   4238.733052  \n",
              "7    3890.357786   3701.039141   3632.463827   3514.096502   3478.341275  \n",
              "8    4617.719937   4606.011141   4606.011141   4481.459284   4456.756441  \n",
              "9       6.965760      6.851832      6.839254      6.621877      6.601376  \n",
              "10     10.079833     10.079833     10.064990      9.956225      9.956225  \n",
              "11     11.163298     11.122316     11.111824     10.926795     10.881152  \n",
              "\n",
              "[12 rows x 54 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-804eeb26-08f6-4db2-9b12-9812a053affb\" class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Optimizer</th>\n",
              "      <th>objfname</th>\n",
              "      <th>ExecutionTime</th>\n",
              "      <th>Individual</th>\n",
              "      <th>Iter1</th>\n",
              "      <th>Iter2</th>\n",
              "      <th>Iter3</th>\n",
              "      <th>Iter4</th>\n",
              "      <th>Iter5</th>\n",
              "      <th>Iter6</th>\n",
              "      <th>...</th>\n",
              "      <th>Iter41</th>\n",
              "      <th>Iter42</th>\n",
              "      <th>Iter43</th>\n",
              "      <th>Iter44</th>\n",
              "      <th>Iter45</th>\n",
              "      <th>Iter46</th>\n",
              "      <th>Iter47</th>\n",
              "      <th>Iter48</th>\n",
              "      <th>Iter49</th>\n",
              "      <th>Iter50</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GA</td>\n",
              "      <td>F3</td>\n",
              "      <td>0.521535</td>\n",
              "      <td>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ...</td>\n",
              "      <td>130570.166419</td>\n",
              "      <td>130570.166419</td>\n",
              "      <td>130570.166419</td>\n",
              "      <td>130570.166419</td>\n",
              "      <td>130570.166419</td>\n",
              "      <td>130570.166419</td>\n",
              "      <td>...</td>\n",
              "      <td>81259.692894</td>\n",
              "      <td>81259.692894</td>\n",
              "      <td>81259.692894</td>\n",
              "      <td>81259.692894</td>\n",
              "      <td>81259.692894</td>\n",
              "      <td>81259.692894</td>\n",
              "      <td>81259.692894</td>\n",
              "      <td>81259.692894</td>\n",
              "      <td>81259.692894</td>\n",
              "      <td>81259.692894</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GA</td>\n",
              "      <td>F3</td>\n",
              "      <td>0.615502</td>\n",
              "      <td>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ...</td>\n",
              "      <td>206974.088239</td>\n",
              "      <td>180538.073053</td>\n",
              "      <td>180538.073053</td>\n",
              "      <td>180538.073053</td>\n",
              "      <td>180538.073053</td>\n",
              "      <td>180538.073053</td>\n",
              "      <td>...</td>\n",
              "      <td>96840.601881</td>\n",
              "      <td>96840.601881</td>\n",
              "      <td>96840.601881</td>\n",
              "      <td>96840.601881</td>\n",
              "      <td>96840.601881</td>\n",
              "      <td>96840.601881</td>\n",
              "      <td>96840.601881</td>\n",
              "      <td>96840.601881</td>\n",
              "      <td>96840.601881</td>\n",
              "      <td>96840.601881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GA</td>\n",
              "      <td>F3</td>\n",
              "      <td>0.569868</td>\n",
              "      <td>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ...</td>\n",
              "      <td>168493.213187</td>\n",
              "      <td>168493.213187</td>\n",
              "      <td>168493.213187</td>\n",
              "      <td>168493.213187</td>\n",
              "      <td>168493.213187</td>\n",
              "      <td>168493.213187</td>\n",
              "      <td>...</td>\n",
              "      <td>74258.692160</td>\n",
              "      <td>74258.692160</td>\n",
              "      <td>74258.692160</td>\n",
              "      <td>74258.692160</td>\n",
              "      <td>74258.692160</td>\n",
              "      <td>74258.692160</td>\n",
              "      <td>74258.692160</td>\n",
              "      <td>74258.692160</td>\n",
              "      <td>74258.692160</td>\n",
              "      <td>74258.692160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GA</td>\n",
              "      <td>F4</td>\n",
              "      <td>0.200179</td>\n",
              "      <td>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ...</td>\n",
              "      <td>91.493117</td>\n",
              "      <td>86.644102</td>\n",
              "      <td>86.644102</td>\n",
              "      <td>86.644102</td>\n",
              "      <td>86.644102</td>\n",
              "      <td>86.644102</td>\n",
              "      <td>...</td>\n",
              "      <td>83.368319</td>\n",
              "      <td>83.368319</td>\n",
              "      <td>83.368319</td>\n",
              "      <td>83.368319</td>\n",
              "      <td>83.368319</td>\n",
              "      <td>83.368319</td>\n",
              "      <td>83.368319</td>\n",
              "      <td>83.368319</td>\n",
              "      <td>83.368319</td>\n",
              "      <td>83.368319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GA</td>\n",
              "      <td>F4</td>\n",
              "      <td>0.251925</td>\n",
              "      <td>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ...</td>\n",
              "      <td>89.737736</td>\n",
              "      <td>89.737736</td>\n",
              "      <td>87.019513</td>\n",
              "      <td>87.019513</td>\n",
              "      <td>87.019513</td>\n",
              "      <td>87.019513</td>\n",
              "      <td>...</td>\n",
              "      <td>81.337952</td>\n",
              "      <td>81.337952</td>\n",
              "      <td>81.337952</td>\n",
              "      <td>81.337952</td>\n",
              "      <td>81.337952</td>\n",
              "      <td>81.337952</td>\n",
              "      <td>81.337952</td>\n",
              "      <td>81.337952</td>\n",
              "      <td>81.337952</td>\n",
              "      <td>81.337952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>GA</td>\n",
              "      <td>F4</td>\n",
              "      <td>0.190003</td>\n",
              "      <td>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ...</td>\n",
              "      <td>90.316431</td>\n",
              "      <td>87.100211</td>\n",
              "      <td>87.100211</td>\n",
              "      <td>86.933688</td>\n",
              "      <td>86.933688</td>\n",
              "      <td>86.933688</td>\n",
              "      <td>...</td>\n",
              "      <td>73.134760</td>\n",
              "      <td>73.134760</td>\n",
              "      <td>73.134760</td>\n",
              "      <td>67.604958</td>\n",
              "      <td>67.604958</td>\n",
              "      <td>67.604958</td>\n",
              "      <td>67.604958</td>\n",
              "      <td>67.604958</td>\n",
              "      <td>67.604958</td>\n",
              "      <td>67.604958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>PSO</td>\n",
              "      <td>F3</td>\n",
              "      <td>1.320900</td>\n",
              "      <td>[ 11.97052812 -22.16233074  10.2017005   10.62...</td>\n",
              "      <td>100570.393233</td>\n",
              "      <td>100570.393233</td>\n",
              "      <td>100570.393233</td>\n",
              "      <td>78863.766888</td>\n",
              "      <td>60687.383599</td>\n",
              "      <td>50019.614952</td>\n",
              "      <td>...</td>\n",
              "      <td>5310.821403</td>\n",
              "      <td>5068.236369</td>\n",
              "      <td>4962.480617</td>\n",
              "      <td>4900.331305</td>\n",
              "      <td>4859.780326</td>\n",
              "      <td>4756.901542</td>\n",
              "      <td>4542.767308</td>\n",
              "      <td>4542.767308</td>\n",
              "      <td>4384.864903</td>\n",
              "      <td>4238.733052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>PSO</td>\n",
              "      <td>F3</td>\n",
              "      <td>1.407495</td>\n",
              "      <td>[ -8.10768215   5.58927723  14.1890242  -35.20...</td>\n",
              "      <td>97804.329254</td>\n",
              "      <td>97804.329254</td>\n",
              "      <td>97804.329254</td>\n",
              "      <td>97804.329254</td>\n",
              "      <td>88667.652076</td>\n",
              "      <td>66881.938917</td>\n",
              "      <td>...</td>\n",
              "      <td>4547.411869</td>\n",
              "      <td>4442.291083</td>\n",
              "      <td>4159.896201</td>\n",
              "      <td>4159.896201</td>\n",
              "      <td>4051.697568</td>\n",
              "      <td>3890.357786</td>\n",
              "      <td>3701.039141</td>\n",
              "      <td>3632.463827</td>\n",
              "      <td>3514.096502</td>\n",
              "      <td>3478.341275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>PSO</td>\n",
              "      <td>F3</td>\n",
              "      <td>1.397210</td>\n",
              "      <td>[  0.17904748  -8.36584397  29.24199282 -32.90...</td>\n",
              "      <td>77048.803686</td>\n",
              "      <td>77048.803686</td>\n",
              "      <td>77048.803686</td>\n",
              "      <td>60590.258165</td>\n",
              "      <td>54788.367680</td>\n",
              "      <td>54788.367680</td>\n",
              "      <td>...</td>\n",
              "      <td>5153.081050</td>\n",
              "      <td>4944.133431</td>\n",
              "      <td>4936.921069</td>\n",
              "      <td>4796.089922</td>\n",
              "      <td>4713.281683</td>\n",
              "      <td>4617.719937</td>\n",
              "      <td>4606.011141</td>\n",
              "      <td>4606.011141</td>\n",
              "      <td>4481.459284</td>\n",
              "      <td>4456.756441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>PSO</td>\n",
              "      <td>F4</td>\n",
              "      <td>0.859286</td>\n",
              "      <td>[ 0.11551122 -1.17811379 -5.95366504 -1.711534...</td>\n",
              "      <td>92.688420</td>\n",
              "      <td>87.098402</td>\n",
              "      <td>81.949001</td>\n",
              "      <td>76.362962</td>\n",
              "      <td>70.717001</td>\n",
              "      <td>65.147688</td>\n",
              "      <td>...</td>\n",
              "      <td>7.064083</td>\n",
              "      <td>7.064083</td>\n",
              "      <td>6.965760</td>\n",
              "      <td>6.965760</td>\n",
              "      <td>6.965760</td>\n",
              "      <td>6.965760</td>\n",
              "      <td>6.851832</td>\n",
              "      <td>6.839254</td>\n",
              "      <td>6.621877</td>\n",
              "      <td>6.601376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>PSO</td>\n",
              "      <td>F4</td>\n",
              "      <td>1.126943</td>\n",
              "      <td>[-9.32644819  6.49707997 -5.61186936 -5.427358...</td>\n",
              "      <td>89.535814</td>\n",
              "      <td>89.099842</td>\n",
              "      <td>83.390472</td>\n",
              "      <td>77.535814</td>\n",
              "      <td>71.940283</td>\n",
              "      <td>66.158472</td>\n",
              "      <td>...</td>\n",
              "      <td>11.516998</td>\n",
              "      <td>11.351656</td>\n",
              "      <td>11.263149</td>\n",
              "      <td>10.771106</td>\n",
              "      <td>10.244018</td>\n",
              "      <td>10.079833</td>\n",
              "      <td>10.079833</td>\n",
              "      <td>10.064990</td>\n",
              "      <td>9.956225</td>\n",
              "      <td>9.956225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>PSO</td>\n",
              "      <td>F4</td>\n",
              "      <td>1.608603</td>\n",
              "      <td>[  9.34763946   2.46966565 -10.42404437  10.40...</td>\n",
              "      <td>90.793971</td>\n",
              "      <td>87.535239</td>\n",
              "      <td>82.986821</td>\n",
              "      <td>77.353088</td>\n",
              "      <td>71.720253</td>\n",
              "      <td>65.956182</td>\n",
              "      <td>...</td>\n",
              "      <td>11.532958</td>\n",
              "      <td>11.489916</td>\n",
              "      <td>11.406231</td>\n",
              "      <td>11.403637</td>\n",
              "      <td>11.276678</td>\n",
              "      <td>11.163298</td>\n",
              "      <td>11.122316</td>\n",
              "      <td>11.111824</td>\n",
              "      <td>10.926795</td>\n",
              "      <td>10.881152</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12 rows × 54 columns</p>\n",
              "</div>\n",
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              "\n",
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              "    .colab-df-convert:hover {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        const element = document.querySelector('#df-804eeb26-08f6-4db2-9b12-9812a053affb');\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "\n",
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              "<style>\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-78da8813-da2d-435a-81b5-4ad77a5b7a8e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7gw9qJb7vedV"
      },
      "source": [
        "<h4>Convergence Curve Plot</h4>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "14hzxM7spueJ",
        "outputId": "bd03c621-cd60-40d6-dbea-8cc0b1270527",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "5fcc2dcfac7d4c999b5c3d063101be73",
            "c9ecfafb6f9b4e0c8dfec25b8d3eb24e",
            "1c6a5d50ca23408eb9de7768f71e1a5f"
          ]
        }
      },
      "source": [
        "#Select convergence curve to show\n",
        "filenames = [filename for filename in os.listdir(foldername) if filename.startswith('convergence')]\n",
        "\n",
        "drop_plot_convergence = widgets.Dropdown(options=filenames, description='Select plot:')\n",
        "drop_plot_convergence"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dropdown(description='Select plot:', options=('convergence-F3.png', 'convergence-F4.png'), value='convergence-…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5fcc2dcfac7d4c999b5c3d063101be73"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZMstknfRafMj",
        "outputId": "5ca200e1-f8f6-4cea-f138-cede8d7d2f09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "source": [
        "#Show selected convergence curve\n",
        "Image(foldername +'/' + drop_plot_convergence.value)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HYEl6buevixm"
      },
      "source": [
        "<h4>Box Plot</h4>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rWF1AKS5bPvV",
        "outputId": "283ba93a-821e-4260-f575-f44de60067d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "c9e86fa2383f4dc6967fcacc2c9bc455",
            "c6074f4505ac4735b9aa7590b413babd",
            "a9e9da0efbe4414f9f5d055b5d88c105"
          ]
        }
      },
      "source": [
        "#Select boxplot to show\n",
        "filenames = [filename for filename in os.listdir(foldername) if filename.startswith('boxplot')]\n",
        "\n",
        "drop_boxplot = widgets.Dropdown(options=filenames, description='Select plot:')\n",
        "drop_boxplot"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dropdown(description='Select plot:', options=('boxplot-F4.png', 'boxplot-F3.png'), value='boxplot-F4.png')"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c9e86fa2383f4dc6967fcacc2c9bc455"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "paBsvZRktfTh",
        "outputId": "c34d6d77-922f-4b68-80f1-40546ce43b19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "source": [
        "#Show selected boxplot\n",
        "Image(foldername +'/' + drop_boxplot.value)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eahYmfKKJ62g"
      },
      "source": [
        "<h2>Citation Request</h2>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PkabkcHFKFIK"
      },
      "source": [
        "Please include these citations if you plan to use this Framework:\n",
        "\n",
        "*   Faris, Hossam, Ibrahim Aljarah, Seyedali Mirjalili, Pedro A. Castillo, and Juan Julián Merelo Guervós. \"EvoloPy: An Open-source Nature-inspired Optimization Framework in Python.\" In IJCCI (ECTA), pp. 171-177. 2016.\n",
        "*   Qaddoura, Raneem, Hossam Faris, Ibrahim Aljarah, and Pedro A. Castillo. \"EvoCluster: An Open-Source Nature-Inspired Optimization Clustering Framework in Python.\" In International Conference on the Applications of Evolutionary Computation (Part of EvoStar), pp. 20-36. Springer, Cham, 2020.\n",
        "*   Ruba Abu Khurma, Ibrahim Aljarah, Ahmad Sharieh, and Seyedali Mirjalili. Evolopy-fs: An open-source nature-inspired optimization framework in python for feature selection. In Evolutionary Machine Learning Techniques, pages 131–173. Springer, 2020"
      ]
    }
  ]
}