research-article

Dynamic multi-swarm particle swarm optimizer with cooperative learning strategy

Authors:

Xinping GuanAuthors Info & Claims

Applied Soft Computing, Volume 29, Issue C

Pages 169 - 183

https://doi.org/10.1016/j.asoc.2014.12.026

Published: 01 April 2015 Publication History

Abstract

Graphical abstractDisplay Omitted HighlightsA new cooperative learning strategy is hybridized with DMS-PSO.Information can be exchanged among sub-swarms before the regrouping process.Experimental results show that DMS-PSO-CLS has a superior performance. In this article, the dynamic multi-swarm particle swarm optimizer (DMS-PSO) and a new cooperative learning strategy (CLS) are hybridized to obtain DMS-PSO-CLS. DMS-PSO is a recently developed multi-swarm optimization algorithm and has strong exploration ability for the use of a novel randomly regrouping schedule. However, the frequently regrouping operation of DMS-PSO results in the deficiency of the exploitation ability. In order to achieve a good balance between the exploration and exploitation abilities, the cooperative learning strategy is hybridized to DMS-PSO, which makes information be used more effectively to generate better quality solutions. In the proposed strategy, for each sub-swarm, each dimension of the two worst particles learns from the better particle of two randomly selected sub-swarms using tournament selection strategy, so that particles can have more excellent exemplars to learn and can find the global optimum more easily. Experiments are conducted on some well-known benchmarks and the results show that DMS-PSO-CLS has a superior performance in comparison with DMS-PSO and several other popular PSO variants.

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Published In

cover image Applied Soft Computing

Applied Soft Computing Volume 29, Issue C

April 2015

487 pages

ISSN:1568-4946

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Copyright © Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 April 2015

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Cited By

Wang RHao KChen LLiu XZhu XZhao C(2024)A modified hybrid particle swarm optimization based on comprehensive learning and dynamic multi-swarm strategySoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-09332-028:5(3879-3903)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s00500-023-09332-0
Pang JLi XHan S(2023)PSO with Mixed Strategy for Global OptimizationComplexity10.1155/2023/71115482023Online publication date: 1-Jan-2023
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