Evolutionary Algorithm with Dynamic Population Size for Constrained Multiobjective Optimization

The core task of constrained multiobjective optimization is to achieve a tradeoff between exploration and exploitation as well as a tradeoff between constraints and objectives. We present an effective evolutionary algorithm with a dynamic population size (DPSEA) to achieve these two tradeoffs. In or...

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Bibliographic Details
Published in:Swarm and evolutionary computation Vol. 73; p. 101104
Main Authors: Wang, Bing-Chuan, Shui, Zhong-Yi, Feng, Yun, Ma, Zhongwei
Format: Journal Article
Language:English
Published: Elsevier B.V 01.08.2022
Subjects:
Constrained multiobjective optimization
dynamic population size
evolutionary algorithm
mating selection
two tradeoffs
Constrained multiobjective optimization
dynamic population size
two tradeoffs
mating selection
evolutionary algorithm
ISSN:2210-6502
Online Access:Get full text
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Summary:The core task of constrained multiobjective optimization is to achieve a tradeoff between exploration and exploitation as well as a tradeoff between constraints and objectives. We present an effective evolutionary algorithm with a dynamic population size (DPSEA) to achieve these two tradeoffs. In order to balance exploration and exploitation, the population size of DPSEA decreases continually as generation increases. In this manner, a bigger population size can encourage exploration in the early stage, while a smaller one can promote exploitation in the later stage. Aiming to balance constraints and objectives, a two-stage environmental selection strategy is proposed. In this strategy, objective information and constraint information is used to select two sets of solutions, respectively. Note that the sizes of these two sets are also adjusted dynamically. In this way, the information of both objectives and constraints can be used. Moreover, a novel mating selection strategy is designed to select promising parents. By assembling the above processes, DPSEA is able to achieve the two tradeoffs which are critical to constrained multiobjective optimization. Experiments on three sets of benchmark test functions owning difficult characteristics validate that DPSEA is competitive against some state-of-the-art constrained multiobjective optimization evolutionary algorithms.
ISSN:2210-6502
DOI:10.1016/j.swevo.2022.101104