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Biogeography-based learning particle swarm optimization
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DOI：10.1007/s00500-016-2307-7.png)
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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.
Keyword:
Particle swarm optimization
Biogeography-based learning
Exemplar generation
Biogeography-based optimization
Migration
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2.5
论文数:
1.0W
被引数:
2.1W
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Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
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