Abstract
This paper presents a new method for effectively searching all global minima of a multimodal function. The method is based on particle swarm optimizer, particles are dynamically divided into serval subgroups of different size in order to explore variable space using various step size simultaneously. In each subgroup, a new scheme is proposed to update the the positions of particles, this scheme takes into consideration the effect of all subgroup seeds. Experimental results for one dimensional, two dimensional and thirty dimensional test suites demonstrated that this method can get overall promising performance over a wide range problems.
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Yuan, X., Peng, J., Nishiura, Y. (2005). Particle Swarm Optimization with Multiscale Searching Method. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596448_99
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DOI: https://doi.org/10.1007/11596448_99
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30818-8
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