A Tractive Population-Assisted Dual-Population and Two-Phase Evolutionary Algorithm for Constrained Multiobjective 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 multiobjective optimization problems. However, most existing algorithms tend to underperform when facing problems with...

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Vydané v:IEEE transactions on evolutionary computation Ročník 29; číslo 1; s. 31 - 45
Hlavní autori: Xie, Shumin, Li, Kangshun, Wang, Wenxiang, Wang, Hui, Peng, Chaoda, Jalil, Hassan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: IEEE 01.02.2025
Predmet:
Classification algorithms
Constrained multiobjective optimization
Convergence
dual-population
evolutionary algorithm
Evolutionary computation
Optimization
Pareto optimization
Sociology
Statistics
two-phase
ISSN:1089-778X, 1941-0026
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Shrnutí:Both dual-population and two-phase strategies are effective for utilizing infeasible solution information and significantly enhancing the ability of algorithms to solve constrained multiobjective optimization problems. However, most existing algorithms tend to underperform when facing problems with complex constraints. To address these issues, a constrained multiobjective evolutionary algorithm named DPTPEA, which combines dual-population and two-phase strategies, is proposed in this article. DPTPEA employs two collaborative populations [the exploitive population (expPop) and the tractive population (tracPop)] and divides the evolutionary process of the tracPop into two phases (Phase 1 and Phase 2). In Phase 1, the tracPop ignores constraints and drags the expPop across the infeasible region by sharing offspring information. In Phase 2, the tracPop adopts the epsilon-constrained method to converge toward the constrained Pareto front and to guide the expPop 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 tracPop 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.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2023.3345470