Implement Adaptive Edge Exploration (AEE) algorithm

Cargo.toml

@@ -39,5 +39,5 @@ name = "aoc-2019-d24"
 path = "adventofcode/2019/d24/d24.rs"
 
 [[bin]]
-name = "dijkstra"
-path = "graph/dijkstra.rs"
+name = "adaptive_edge_exploration"
+path = "graph/adaptive_edge_exploration.rs"

graph/adaptive_edge_exploration.rs

@@ -0,0 +1,184 @@
+use std::collections::{HashMap, VecDeque};
+use rand::Rng;
+
+// Adaptive Edge Exploration (AEE) Algorithm
+//
+// This algorithm explores a graph by adaptively choosing edges based on their
+// weights and the graph's structure. It aims to find paths that balance between
+// shortest distance and interesting graph features, introducing some randomness
+// to explore diverse paths.
+
+// Define the Graph structure
+struct Graph {
+    edges: HashMap<usize, Vec<(usize, usize)>>,
+}
+
+// Define the Node structure for the exploration queue
+#[derive(Clone, Eq, PartialEq)]
+struct Node {
+    vertex: usize,
+    path: Vec<usize>,
+    total_cost: usize,
+    interest_score: f64,
+}
+
+impl Graph {
+    // Constructor for Graph
+    fn new() -> Self {
+        Graph {
+            edges: HashMap::new(),
+        }
+    }
+
+    // Method to add an edge to the graph
+    fn add_edge(&mut self, from: usize, to: usize, cost: usize) {
+        self.edges.entry(from).or_insert(Vec::new()).push((to, cost));
+        self.edges.entry(to).or_insert(Vec::new()); // Ensure all vertices are in the map
+    }
+
+    // Adaptive Edge Exploration (AEE) algorithm
+    fn adaptive_edge_exploration(&self, start: usize, end: usize) -> Option<(Vec<usize>, usize)> {
+        let mut rng = rand::thread_rng();
+        let mut queue = VecDeque::new();
+        let mut visited = HashMap::new();
+
+        queue.push_back(Node {
+            vertex: start,
+            path: vec![start],
+            total_cost: 0,
+            interest_score: 0.0,
+        });
+
+        while let Some(current) = queue.pop_front() {
+            if current.vertex == end {
+                return Some((current.path, current.total_cost));
+            }
+
+            if let Some(&prev_cost) = visited.get(&current.vertex) {
+                if current.total_cost >= prev_cost {
+                    continue;
+                }
+            }
+
+            visited.insert(current.vertex, current.total_cost);
+
+            if let Some(neighbors) = self.edges.get(&current.vertex) {
+                let mut adaptive_neighbors = neighbors.clone();
+                adaptive_neighbors.sort_by(|a, b| {
+                    let a_score = self.calculate_interest_score(a.0, &current);
+                    let b_score = self.calculate_interest_score(b.0, &current);
+                    b_score.partial_cmp(&a_score).unwrap()
+                });
+
+                for &(neighbor, edge_cost) in adaptive_neighbors.iter() {
+                    if !visited.contains_key(&neighbor) || current.total_cost + edge_cost < visited[&neighbor] {
+                        let mut new_path = current.path.clone();
+                        new_path.push(neighbor);
+                        let new_interest_score = self.calculate_interest_score(neighbor, &current);
+
+                        queue.push_back(Node {
+                            vertex: neighbor,
+                            path: new_path,
+                            total_cost: current.total_cost + edge_cost,
+                            interest_score: new_interest_score,
+                        });
+                    }
+                }
+            }
+
+            // Introduce some randomness to explore different paths
+            if rng.gen::<f64>() < 0.1 {
+                queue.make_contiguous().shuffle(&mut rng);
+            }
+        }
+
+        None // No path found
+    }
+
+    // Calculate interest score for a neighbor
+    fn calculate_interest_score(&self, neighbor: usize, current: &Node) -> f64 {
+        let neighbor_degree = self.edges.get(&neighbor).map_or(0, |edges| edges.len());
+        let path_length = current.path.len() as f64;
+        let total_cost = current.total_cost as f64;
+
+        // Combine factors to create an interest score
+        (neighbor_degree as f64).sqrt() + (1.0 / (path_length + 1.0)) + (1.0 / (total_cost + 1.0))
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_simple_path() {
+        let mut graph = Graph::new();
+        graph.add_edge(0, 1, 4);
+        graph.add_edge(1, 2, 3);
+        graph.add_edge(2, 3, 2);
+
+        let result = graph.adaptive_edge_exploration(0, 3);
+        assert!(result.is_some());
+        let (path, cost) = result.unwrap();
+        assert_eq!(path, vec![0, 1, 2, 3]);
+        assert_eq!(cost, 9);
+    }
+
+    #[test]
+    fn test_multiple_paths() {
+        let mut graph = Graph::new();
+        graph.add_edge(0, 1, 4);
+        graph.add_edge(0, 2, 1);
+        graph.add_edge(1, 3, 1);
+        graph.add_edge(2, 1, 2);
+        graph.add_edge(2, 3, 5);
+
+        let result = graph.adaptive_edge_exploration(0, 3);
+        assert!(result.is_some());
+        let (path, cost) = result.unwrap();
+        assert!(path.starts_with(&[0]) && path.ends_with(&[3]));
+        assert!(cost <= 6); // The cost should be at most 6 (0->2->1->3 or 0->1->3)
+    }
+
+    #[test]
+    fn test_no_path() {
+        let mut graph = Graph::new();
+        graph.add_edge(0, 1, 4);
+        graph.add_edge(1, 2, 3);
+        graph.add_edge(3, 4, 2);
+
+        let result = graph.adaptive_edge_exploration(0, 4);
+        assert_eq!(result, None);
+    }
+
+    #[test]
+    fn test_graph_with_cycle() {
+        let mut graph = Graph::new();
+        graph.add_edge(0, 1, 1);
+        graph.add_edge(1, 2, 2);
+        graph.add_edge(2, 3, 3);
+        graph.add_edge(3, 1, 1);
+        graph.add_edge(3, 4, 4);
+
+        let result = graph.adaptive_edge_exploration(0, 4);
+        assert!(result.is_some());
+        let (path, cost) = result.unwrap();
+        assert!(path.starts_with(&[0]) && path.ends_with(&[4]));
+        assert!(cost <= 10); // The cost should be at most 10 (0->1->2->3->4)
+    }
+
+    #[test]
+    fn test_large_graph() {
+        let mut graph = Graph::new();
+        for i in 0..1000 {
+            graph.add_edge(i, i + 1, 1);
+        }
+        graph.add_edge(0, 1000, 999);
+
+        let result = graph.adaptive_edge_exploration(0, 1000);
+        assert!(result.is_some());
+        let (path, cost) = result.unwrap();
+        assert!(path.starts_with(&[0]) && path.ends_with(&[1000]));
+        assert!(cost <= 999); // The cost should be at most 999 (direct path from 0 to 1000)
+    }
+}