Chen, Xu

Du, Wenli

Liu, Guohai

Mei, Congli

Tianfield, Huaglory

English

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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Biogeography-based learning particle swarm optimization

https://researchonline.gcu.ac.uk/files/24558280/H.Tianfield_BLPSO.pdf

Biogeography-based learning particle swarm optimization

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