An adaptive particle swarm optimization method based on clustering

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• 作者：Xiaolei Liang (1)
  Wenfeng Li (1)
  Yu Zhang (1)
  MengChu Zhou (1) (2)
  
  1. School of Logistics Engineering ; Wuhan University of Technology ; Wuhan ; 430063 ; Hubei ; China
  2. Department of Electrical and Computer Engineering ; New Jersey Institute of Technology ; Newark ; NJ ; 07102-1982 ; USA
  
• 关键词：Particle swarm optimization (PSO) ; Function optimization ; Dynamic topology ; Cluster evaluation ; Adaptive particle swarm optimization
• 刊名：Soft Computing - A Fusion of Foundations, Methodologies and Applications
• 出版年：2015
• 出版时间：February 2015
• 年：2015
• 卷：19
• 期：2
• 页码：431-448
• 全文大小：1,014 KB
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• 刊物类别：Engineering
• 刊物主题：Numerical and Computational Methods in Engineering
  Theory of Computation
  Computing Methodologies
  Mathematical Logic and Foundations
  Control Engineering
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1433-7479

文摘

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.