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An adaptive particle swarm optimization method based on clustering

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Abstract

Particle swarm optimization (PSO) is an effective method for solving a wide range of problems. However, the most existing PSO algorithms easily trap into local optima when solving complex multimodal function optimization problems. This paper presents a variation, called adaptive PSO based on clustering (APSO-C), by considering the population topology and individual behavior control together to balance local and global search in an optimization process. APSO-C has two steps. First, via a K-means clustering operation, it divides the swarm dynamically in the whole process to construct variable subpopulation clusters and after that adopts a ring neighborhood topology for information sharing among these clusters. Then, an adaption mechanism is proposed to adjust the inertia weight of all individuals based on the evaluation results of the states of clusters and the swarm, thereby giving the individual suitable search power. The experimental results of fourteen benchmark functions show that APSO-C has better performance in the terms of convergence speed, solution accuracy and algorithm reliability than several other PSO algorithms.

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Acknowledgments

This work was supported in part by the Key Program of Natural Science Foundation of Hubei Province (No. 2010CDA022), the international cooperation project of Hubei province (No. 2011BFA012), National Natural Science Foundation of China (No. 71372202), the national key Technology Research and Development Program (Nos. 2012BAJ05B07 and 2014BAH24F03), the PRC Ministry of Science and Technology under Contract No. 2013DFM10100, the Fundamental Research Funds for the Central Universities (WUT: 2013-IV-057), and the Opening Project of Traffic Transport Industry Key Laboratory of Port Handing Technology (No. 2013-gkzx-k-01).

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Authors and Affiliations

  1. School of Logistics Engineering, Wuhan University of Technology, Wuhan, 430063, Hubei, China

    Xiaolei Liang, Wenfeng Li, Yu Zhang & MengChu Zhou

  2. Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, 07102-1982, USA

    MengChu Zhou

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  1. Xiaolei Liang
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  2. Wenfeng Li
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  3. Yu Zhang
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  4. MengChu Zhou
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Correspondence to Wenfeng Li.

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Communicated by W. Pedrycz.

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Liang, X., Li, W., Zhang, Y. et al. An adaptive particle swarm optimization method based on clustering. Soft Comput 19, 431–448 (2015). https://doi.org/10.1007/s00500-014-1262-4

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