A novel tri-stage with reward-switching mechanism for constrained multiobjective optimization problems

Autor: Jiqing Qu, Xuefeng Li, Hui Xiao
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Complex & Intelligent Systems, Vol 10, Iss 3, Pp 4625-4655 (2024)
Druh dokumentu: article
ISSN: 2199-4536
2198-6053
DOI: 10.1007/s40747-024-01379-2
Popis: Abstract The effective exploitation of infeasible solutions plays a crucial role in addressing constrained multiobjective optimization problems (CMOPs). However, existing constrained multiobjective optimization evolutionary algorithms (CMOEAs) encounter challenges in effectively balancing objective optimization and constraint satisfaction, particularly when tackling problems with complex infeasible regions. Subsequent to the prior exploration, this paper proposes a novel tri-stage with reward-switching mechanism framework (TSRSM), including the push, pull, and repush stages. Each stage consists of two coevolutionary populations, namely $${\text {Pop}}_1$$ Pop 1 and $${\text {Pop}}_2$$ Pop 2 . Throughout the three stages, $${\text {Pop}}_1$$ Pop 1 is tasked with converging to the constrained Pareto front (CPF). However, $${\text {Pop}}_2$$ Pop 2 is assigned with distinct tasks: (i) converging to the unconstrained Pareto front (UPF) in the push stage; (ii) utilizing constraint relaxation technique to discover the CPF in the pull stage; and (iii) revisiting the search for the UPF through knowledge transfer in the repush stage. Additionally, a novel reward-switching mechanism (RSM) is employed to transition between different stages, considering the extent of changes in the convergence and diversity of populations. Finally, the experimental results on three benchmark test sets and 30 real-world CMOPs demonstrate that TSRSM achieves competitive performance when compared with nine state-of-the-art CMOEAs. The source code is available at https://github.com/Qu-jq/TSRSM .
Databáze: Directory of Open Access Journals
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