Abstract
Based on the research of optimal foraging theory (OFT), we present a novel particle swarm optimizer (PSO) to improve the performance of standard PSO (SPSO). The resulting algorithm is known as PSOOFT that makes use of two mechanisms of OFT: a reproduction strategy to enhance the ability to converge rapidly to good solutions and a patch-choice based scheme to keep a right balance of exploration and exploitation. In the simulation studies, several benchmark functions are performed, and the performance of the proposed algorithm is compared to the standard PSO (SPSO). The experimental results show that the PSOOFT prevents premature convergence to a high degree, but still has a more rapid convergence rate than SPSO.
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Niu, B., Zhu, Y., Hu, K., Li, S., He, X. (2006). A Novel Particle Swarm Optimizer Using Optimal Foraging Theory. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence and Bioinformatics. ICIC 2006. Lecture Notes in Computer Science(), vol 4115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816102_7
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DOI: https://doi.org/10.1007/11816102_7
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