A tri-population based co-evolutionary framework for constrained multi-objective optimization problems

•Atri-population based co-evolutionary framework is proposed for CMOPs.•These populations are co-evolved for different problems.•Experiments verified the superiority and the versatility of the framework. Balancing between the optimization of objective functions and constraint satisfaction is essenti...

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Vydáno v:Swarm and evolutionary computation Ročník 70; s. 101055
Hlavní autoři: Ming, Fei, Gong, Wenyin, Wang, Ling, Lu, Chao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.04.2022
Témata:
Co-evolution
Constrained multi-objective optimization
Evolutionary algorithm
Tri-population
Constrained multi-objective optimization
Co-evolution
Tri-population
Evolutionary algorithm
ISSN:2210-6502
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Shrnutí:•Atri-population based co-evolutionary framework is proposed for CMOPs.•These populations are co-evolved for different problems.•Experiments verified the superiority and the versatility of the framework. Balancing between the optimization of objective functions and constraint satisfaction is essential to handle constrained multi-objective optimization problems (CMOPs). Recently, various methods have been presented to enhance the performance for the constrained multi-objective optimization evolutionary algorithms (CMOEAs). However, most of them encounter difficulties when dealing with the CMOPs with complex feasible regions. To overcome this drawback, this paper proposes a tri-population based co-evolutionary framework (TriP): i) the first and second populations are evolved through a weak co-evolutionary relation for the original and unconstrained problems respectively to handle CMOPs with relatively simple constraints; and ii) the third population is evolved solely for the constraint relaxed problem with constraint relaxation technique. The cooperation of three populations preserve the advantages of weak co-evolution and constraint relaxation. Experiments on six benchmark CMOPs with 65 instances and diverse features are performed. Compared to 9 state-of-the-art CMOEAs, the proposed framework yields highly competitive performance and the best versatility. In addition, the effectiveness of the proposed framework on handling real-world CMOPs is also verified.
ISSN:2210-6502
DOI:10.1016/j.swevo.2022.101055