A Dual-Stage Dual-Population Evolutionary Algorithm Using New Adaptive Environmental Selection Method for Complex Constrained Multi-Objective Optimization

Constrained multi-objective optimization problems (CMOPs) are prevalent across various fields. In recent years, numerous constrained multi-objective evolutionary algorithms (CMOEAs) have been developed to tackle these challenges. However, existing CMOEAs still encounter significant challenges when addressing complex CMOPs. For instance, tackling CMOPs characterized by very narrow or disconnected feasible regions poses a significant challenge for current algorithms, particularly in simultaneously ensuring solution feasibility, diversity, and convergence. To address this limitation, this paper proposes DDCEO, a dual-stage dual-population evolutionary algorithm using new adaptive environmental selection method. Within DDCEO, the main population primarily focuses on ensuring solution feasibility, while the auxiliary population assists in enhancing diversity and convergence. The evolutionary process of the auxiliary population is divided into two distinct phases: Phase 1 focuses on global exploration by disregarding constraints to help locate potential feasible domains, while Phase 2 introduces an adaptive environmental selection method that enhances diversity and convergence by strategically retaining non-dominated infeasible solutions positioned far from the main population yet close to the constraint boundaries. Finally, comprehensive experiments on three benchmark test suites demonstrate that the proposed DDCEO algorithm shows superior performance against seven state-of-the-art CMOEAs, namely tDEACPBI, DDCMOEA, ToP, CCMO, BiCo, MTCMO, and CMOEMT. Statistical tests using the IGD and HV metrics confirm that DDCEO holds a significant advantage on the majority of complex test problems.