A Tractive Population Assisted Dual-Population and Two-Phase Evolutionary Algorithm for Constrained Multi-Objective Optimization

Both dual-population and two-phase strategies are effective for utilizing infeasible solution information and significantly enhancing the ability of algorithms to solve constrained multi-objective optimization problems. However, most existing algorithms tend to underperform when facing problems with complex constraints. To address these issues, a constrained multi-objective evolutionary algorithm named DPTPEA, which combines dual-population and two-phase strategies, is proposed in this paper. DPTPEA employs two collaborative populations (the exploitive population and the tractive population) and divides the evolutionary process of the tractive population into two phases (Phase 1 and Phase 2). In Phase 1, the tractive population ignores constraints and drags the exploitive population across the infeasible region by sharing offspring information. In Phase 2, the tractive population adopts the epsilon-constrained method to converge toward the constrained Pareto front and to guide the exploitive population exploiting different feasible regions. Moreover, a dynamic cooperation strategy, a boundary point direction sampling strategy, and a dynamic environmental selection are proposed to improve the exploration ability of tractive population for solving complex problems. Comprehensive experiments on three popular test suites demonstrate that DPTPEA outperforms seven state-of-the-art algorithms on most test problems.