* articles.bib (LiLopYao2023archiving): New.

articles.bib

@@ -10510,6 +10510,46 @@ @Article{LiLiTanYao2015many
   numpages =     35,
 }
 
+@Article{LiLopYao2023archiving,
+  author       = Li_Miqing #and# Lopez-Ibanez #and# Yao_Xin,
+  title        = {Multi-Objective Archiving},
+  journal      = tec,
+  year         = 2023,
+  doi          = {10.1109/TEVC.2023.3314152},
+  abstract     = {Most multi-objective optimisation algorithms maintain an
+                  archive explicitly or implicitly during their search. Such an
+                  archive can be solely used to store high-quality solutions
+                  presented to the decision maker, but in many cases may
+                  participate in the search process (e.g., as the population in
+                  evolutionary computation). Over the last two decades,
+                  archiving, the process of comparing new solutions with
+                  previous ones and deciding how to update the
+                  archive/population, stands as an important issue in
+                  evolutionary multi-objective optimisation (EMO). This is
+                  evidenced by constant efforts from the community on
+                  developing various effective archiving methods, ranging from
+                  conventional Pareto-based methods to more recent
+                  indicator-based and decomposition-based ones. However, the
+                  focus of these efforts is on empirical performance comparison
+                  in terms of specific quality indicators; there is lack of
+                  systematic study of archiving methods from a general
+                  theoretical perspective. In this paper, we attempt to conduct
+                  a systematic overview of multi-objective archiving, in the
+                  hope of paving the way to understand archiving algorithms
+                  from a holistic perspective of theory and practice, and more
+                  importantly providing a guidance on how to design
+                  theoretically desirable and practically useful archiving
+                  algorithms. In doing so, we also present that archiving
+                  algorithms based on weakly Pareto compliant indicators (e.g.,
+                  -indicator), as long as designed properly, can achieve the
+                  same theoretical desirables as archivers based on Pareto
+                  compliant indicators (e.g., hypervolume indicator). Such
+                  desirables include the property limit-optimal, the limit form
+                  of the possible optimal property that a bounded archiving
+                  algorithm can have with respect to the most general form of
+                  superiority between solution sets.}
+}
+
 @Article{LiTanLiYao2016stochastic,
   title =        {Stochastic ranking algorithm for many-objective optimization
                   based on multiple indicators},