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A coevolutionary algorithm based on reference line guided archive for constrained multiobjective optimization
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AI
DOI：10.1016/j.asoc.2023.110169.png)
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Abstract
En 中文
The objective space of the constrained multiobjective optimization problem (CMOP) is constantly torn by the applied constraints. This makes evolutionary algorithms, which are driven by objectives, face greater difficulties in feasibility, convergence, and diversity. Most evolutionary algorithms will be trapped in local optimums such as a fake Pareto-optimal front or a mutilated Pareto-optimal front. To address this issue, this paper proposes an archive-assisted evolutionary framework with a novel archive structure and cooperative mechanism. A reference line guided archive (RA) is established to record the evolution of the unconstrained solutions. The updated criteria of RA are based on the distance from the individual to the reference line and the unconstrained dominance relation. A specially designed adaptive mating selection operator will select mating parents from RA and the main population according to the convergence of the main population and RA, respectively. The participation of RA in offspring reproduction is conducive to skipping infeasible regions for extensive searches. The performance of the archive-assisted nondominated sorting genetic algorithm (AA-NSGA), which embeds the proposed archive strategy into the nondominated sorting genetic algorithm II, is compared with four state-of-the-art constrained multiobjective evolutionary algorithms (MOEAs). The experimental results on 38 benchmark CMOPs and a reactor network design problem show that the proposed AA-NSGA has a high performance among the existing MOEAs in terms of feasibility, convergence, and diversity. (c) 2023 Published by Elsevier B.V.
Keywords:
Evolutionary algorithm
Nondominated sorting
Constrained multiobjective optimization
Archive strategy
Cooperative mechanism