Three-Role-Community Evolutionary Algorithm for Constrained Multi-objective Optimization Problems

Abstract

Inspired by the concept of divide-and-conquer, existing multi-task/ multi-population constraint evolutionary algorithms (CMOEAs) have often employed an auxiliary population that disregards all constraints in order to simplify the problem. However, when dealing with complex Constraint Pareto Fronts (CPF), many existing approaches encounter difficulties in maintaining diversity and avoiding local optima. To address the above issue, the Three-role-community based CMOEA (TRC) which focuses on roles within the population is introduced to eliminate the burden of knowledge transfer between multi-task or multi-population CMOEAs. TRC establishes three essential roles: the feasible group, tasked with identifying CPFs; the exploration group, dedicated to discovering the unconstrained Pareto Front (UPF); and the diversity group, responsible for preserving population diversity. By dynamically adjusting the allocation of individuals to these roles, TRC effectively navigates the evolving problem landscape. Moreover, a flexible and straightforward quota allocation strategy for offspring size is designed in TRC. Rigorously tested on MW and DASCMOP test suites, TRC’s performance is either better than or at least comparable to some state-of-the-art algorithms.