Authors: Chengyu Hu, Bo Wang, Yongji Wang
Addresses: Department of Control Science and Engineering, Huazhong University of Science and Technology, 388 Lumo Road, Wuhan 430074, China. ' Department of Automation, Shanghai Jiaotong University, 800 Dongchuan Road, Min Hang, Shanghai, 200240, China. ' Department of Control Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China
Abstract: Many real-world problems are dynamic, requiring an optimisation algorithm which is able to continuously track a changing optimum over time. In this paper, we present a new variant of particle swarm optimisation (PSO) specifically designed to work well in dynamic environments. The main idea is to divide the population of particles into a set of interacting swarms. These swarms interact locally by dynamic regrouping and dispersing. Cauchy mutation is applied to the global best particle when the swarm detects the environment of the change. The dynamic function [proposed by Morrison and De Jong (1999)] is used to test the performance of the proposed algorithm. The numerical experimental results are compared with other variant PSO from the literature, showing that the proposed algorithm is an excellent alternative to track dynamically changing optima.
Keywords: multiple swarms; Cauchy mutation; dynamic optimisation; particle swarm optimisation; multi-swarm PSO.
International Journal of Innovative Computing and Applications, 2009 Vol.2 No.2, pp.123 - 132
Published online: 24 Feb 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article