Biogeography-based learning particle swarm optimization
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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.
KeywordsParticle swarm optimization Biogeography-based learning Exemplar generation Biogeography-based optimization Migration
This work was partly supported by the Research Talents Startup Foundation of Jiangsu University (Grant No. 15JDG139), the China Postdoctoral Science Foundation (Grant No. 2016M591783), and the Natural Science Foundation of Jiangsu Province (Grant No. BK20160540). The authors would like to especially thank Dr. Wenyin Gong for his helpful comments on work of this paper. The authors would appreciate the scientific efforts of Dr. N. Hansen, Dr. C. Garcia-Martinez, Dr. J. Zhang, and Dr. Y. Jin in making available the source codes of CMAES, GL-25, JADE, and SL-PSO, and Dr. P. N. Suganthan for providing the source codes of CLPSO, DMSPSO, and SaDE.
Compliance with ethical standards
Conflict of interest
The author declares that there is no conflict of interest.
The work of this article does not involve use of human participants or animals.
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