In this paper, five previous Particle Swarm Optimization (PSO) algorithms for multimodal function optimization are reviewed. A new and a successful PSO based algorithm, named as CPSO is proposed. CPSO enhances the exploration and exploitation capabilities of PSO by performing search using a random walk and a hill climbing components. Furthermore, one of the previous PSO approaches is improved incredibly by means of a minor adjustment. All algorithms are compared over a set of well-known benchmark functions. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Özcan, E., & Yilmaz, M. (2007). Particle swarms for multimodal optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4431 LNCS, pp. 366–375). Springer Verlag. https://doi.org/10.1007/978-3-540-71618-1_41
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