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08.08.2016 | Methodologies and Application

Biogeography-based learning particle swarm optimization

verfasst von: Xu Chen, Huaglory Tianfield, Congli Mei, Wenli Du, Guohai Liu

Erschienen in: Soft Computing | Ausgabe 24/2017

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Abstract

This paper explores biogeography-based learning particle swarm optimization (BLPSO). Specifically, based on migration of biogeography-based optimization (BBO), a new biogeography-based learning strategy is proposed for particle swarm optimization (PSO), whereby each particle updates itself by using the combination of its own personal best position and personal best positions of all other particles through the BBO migration. The proposed BLPSO is thoroughly evaluated on 30 benchmark functions from CEC 2014. The results are very promising, as BLPSO outperforms five well-established PSO variants and several other representative evolutionary algorithms.

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webpage of BBO http://embeddedlab.csuohio.edu/BBO/.

The source codes of DMSPSO, FIPS, and CLPSO are provided by Dr. P.N. Suganthan, and the source code of SL-PSO is downloaded from Dr. Y. Jin’s homepage http://www.surrey.ac.uk/cs/research/nice/people/yaochu_jin/.

The source codes of CMAES, GL-25, and JADE are downloaded from Dr. Y. Wang’s homepage http://ist.csu.edu.cn/YongWang.htm.

The source code of our proposed BLPSO is available from the first author upon request.

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Titel: Biogeography-based learning particle swarm optimization
verfasst von: Xu Chen
Huaglory Tianfield
Congli Mei
Wenli Du
Guohai Liu
Publikationsdatum: 08.08.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 24/2017
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-016-2307-7

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