Evolutionary Multi-Objective Optimization Algorithm Framework with Three Solution Sets
Hisao Ishibuchi, Lie Meng Pang, Ke Shang

TL;DR
This paper proposes a flexible evolutionary multi-objective optimization framework with three solution sets to better accommodate different decision-making needs, enhancing solution presentation and explainability.
Contribution
It introduces a general EMO framework with three solution sets, allowing customizable population, archive, and final solution sets for diverse decision-making scenarios.
Findings
Framework outperforms standard approaches in experiments
Allows flexible solution set sizes and selection
Provides a basis for explainable EMO methods
Abstract
It is assumed in the evolutionary multi-objective optimization (EMO) community that a final solution is selected by a decision maker from a non-dominated solution set obtained by an EMO algorithm. The number of solutions to be presented to the decision maker can be totally different. In some cases, the decision maker may want to examine only a few representative solutions from which a final solution is selected. In other cases, a large number of non-dominated solutions may be needed to visualize the Pareto front. In this paper, we suggest the use of a general EMO framework with three solution sets to handle various situations with respect to the required number of solutions. The three solution sets are the main population of an EMO algorithm, an external archive to store promising solutions, and a final solution set which is presented to the decision maker. The final solution set is…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Multi-Objective Optimization Algorithms · Metaheuristic Optimization Algorithms Research · Evolutionary Algorithms and Applications
