caa a22 4500 605470715 CHVBK 20210128100329.0 cr unu---uuuuu 210128e20150201xx s 000 0 eng 10.1007/s00500-014-1262-4 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1262-4 An adaptive particle swarm optimization method based on clustering [Elektronische Daten] [Xiaolei Liang, Wenfeng Li, Yu Zhang, MengChu Zhou] 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. Springer-Verlag Berlin Heidelberg, 2014 Particle swarm optimization (PSO) nationallicence Function optimization nationallicence Dynamic topology nationallicence Cluster evaluation nationallicence Adaptive particle swarm optimization nationallicence Liang Xiaolei School of Logistics Engineering, Wuhan University of Technology, 430063, Wuhan, Hubei, China aut Li Wenfeng School of Logistics Engineering, Wuhan University of Technology, 430063, Wuhan, Hubei, China aut Zhang Yu School of Logistics Engineering, Wuhan University of Technology, 430063, Wuhan, Hubei, China aut Zhou MengChu School of Logistics Engineering, Wuhan University of Technology, 430063, Wuhan, Hubei, China aut Soft Computing Springer Berlin Heidelberg 19/2(2015-02-01), 431-448 1432-7643 19:2<431 2015 19 500 https://doi.org/10.1007/s00500-014-1262-4 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1262-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Liang Xiaolei School of Logistics Engineering, Wuhan University of Technology, 430063, Wuhan, Hubei, China aut NATIONALLICENCE 700 1- Li Wenfeng School of Logistics Engineering, Wuhan University of Technology, 430063, Wuhan, Hubei, China aut NATIONALLICENCE 700 1- Zhang Yu School of Logistics Engineering, Wuhan University of Technology, 430063, Wuhan, Hubei, China aut NATIONALLICENCE 700 1- Zhou MengChu School of Logistics Engineering, Wuhan University of Technology, 430063, Wuhan, Hubei, China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/2(2015-02-01), 431-448 1432-7643 19:2<431 2015 19 500