Decomposition-based dual-population evolutionary algorithm for constrained multi-objective problem

Constrained multi-objective optimization problems require optimizing and solving multiple objectives while satisfying the constraints. However, in the process of solving this problem, some constraints created infeasible obstacle regions, which led to the neglect of a portion of the constrained Paret...

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Vydáno v:Swarm and evolutionary computation Ročník 95; s. 101912
Hlavní autoři: Wang, Yufeng, Zhang, Yong, Xu, Chunyu, Bai, Wen, Zheng, Ke, Dong, Wenyong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.06.2025
Témata:
Angle-based constraint dominance principle
Constrained multi-objective evolutionary algorithm
Dual-population
Two stage
Two stage
Constrained multi-objective evolutionary algorithm
Dual-population
Angle-based constraint dominance principle
ISSN:2210-6502
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Shrnutí:Constrained multi-objective optimization problems require optimizing and solving multiple objectives while satisfying the constraints. However, in the process of solving this problem, some constraints created infeasible obstacle regions, which led to the neglect of a portion of the constrained Pareto front (CPF). In order to solve this problem, A novel decomposition-based dual-population constrained multi-objective evolutionary algorithm (DD-CMOEA) is proposed. DD-CMOEA adopts a dual population collaborative search strategy, which can quickly find CPF. In the first stage, DD-CMOEA conducts dual population searches on CPF and unconstrained Pareto front (UPF) separately. During the search process, sub-population A uses unconstrained global exploration to obtain information that helps sub-population B jump through infeasible obstacle areas. In the second stage, when the convergence of the sub-population searching for UPF stagnates, the angle-based constraint advantage principle is used for reverse search. It ensures that the searched CPF solution set can be evenly distributed throughout the entire search space. The experimental results on three standard benchmark function suites show that DD-CMOEA outperforms the other six state-of-the-art algorithms in solving constrained multi-objective optimization problems.
ISSN:2210-6502
DOI:10.1016/j.swevo.2025.101912