Adaptive Population Sizing for Multi-Population Based Constrained Multi-Objective Optimization

Abstract

In recent years, the prevalence of constrained multi-objective optimization problems across numerous scenarios has incited a surge of interest in the advancement of constrained multi-objective evolutionary algorithms (CMOEAs). To handle objectives and constraints separately, multi-population CMOEAs have demonstrated effectiveness in balancing between objective optimization and constraint satisfaction. Nevertheless, the evolution of the auxiliary population often necessitates an equal or even greater number of function evaluations compared to the main population, leading to substantial expenditure of computational resources. In view of the varying difficulty of constraint satisfaction across distinct problems, this paper suggests an adaptive population sizing method to dynamically shrink the auxiliary population according to the current evolutionary state. Subsequently, a multistage evolutionary algorithm is developed, which integrates a variety of strategies to more effectively evolve the auxiliary population and ultimately eliminate it, thereby saving computational resources for the main population. The proposed CMOEA is empirically evaluated against nine state-of-the-art algorithms on five challenging test suites, which exhibits superior performance and versatility.

Keywords: Constrained optimizationMulti-objective optimizationMulti-population evolutionMulti-stage evolutionAdaptive population sizing