A dual-population Constrained Many-Objective Evolutionary Algorithm based on reference point and angle easing strategy.

ABSTRACT: Constrained many-objective optimization problems (CMaOPs) have gradually emerged in various areas and are significant for this field. These problems often involve intricate Pareto frontiers (PFs) that are both refined and uneven, thereby making their resolution difficult and challenging. Traditional algorithms tend to over prioritize convergence, leading to premature convergence of the decision variables, which greatly reduces the possibility of finding the constrained Pareto frontiers (CPFs). This results in poor overall performance. To tackle this challenge, our solution involves a novel dual-population constrained many-objective evolutionary algorithm based on reference point and angle easing strategy (dCMaOEA-RAE). It relies on a relaxed selection strategy utilizing reference points an

SUBMITTER: Ji C 

PROVIDER: S-EPMC11323086 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature