Two-stage differential evolution with dynamic population assignment for constrained multi-objective optimization

Using infeasible information to balance objective optimization and constraint satisfaction is a very promising research direction to address constrained multi-objective problems (CMOPs) via evolutionary algorithms (EAs). The existing constrained multi-objective evolutionary algorithms (CMOEAs) still...

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Vydáno v:Swarm and evolutionary computation Ročník 90; s. 101657
Hlavní autoři: Xu, Bin, Zhang, Haifeng, Tao, Lili
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.10.2024
Témata:
Constrained multi-objective optimization
Differential evolution
Dynamic population assignment
Infeasible information
Multiple strategies
Two stages
Constrained multi-objective optimization
Differential evolution
Infeasible information
Two stages
Dynamic population assignment
Multiple strategies
ISSN:2210-6502
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Shrnutí:Using infeasible information to balance objective optimization and constraint satisfaction is a very promising research direction to address constrained multi-objective problems (CMOPs) via evolutionary algorithms (EAs). The existing constrained multi-objective evolutionary algorithms (CMOEAs) still face the issue of striking a good balance when solving CMOPs with diverse characteristics. To alleviate this issue, in this paper we develop a two-stage different evolution with a dynamic population assignment strategy for CMOPs. In this approach, two cooperative populations are used to provide feasible driving forces and infeasible guiding knowledge. To adequately utilize the infeasibility information, a dynamic population assignment model is employed to determine the primary population, which is used as the parents to generate offspring. The entire search process is divided into two stages, in which the two populations work in weak and strong cooperative ways, respectively. Furthermore, multistrategy-based differential evolution operators are adopted to create aggressive offspring. The superior exploration and exploitation ability of the proposed algorithm is validated via some state-of-the-art CMOEAs over artificial benchmarks and real-world problems. The experimental results show that our proposed algorithm gained a better, or more competitive, performance than the other competitors, and it is an effective approach to balancing objective optimization and constraint satisfaction.
ISSN:2210-6502
DOI:10.1016/j.swevo.2024.101657