Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/5817
Title: A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments
Authors: Yang, S
Li, C
Keywords: Clustering;Dynamic optimization problem (DOP);Local search;Multiswarm;Particle swarm optimization
Issue Date: 2010
Publisher: IEEE
Citation: IEEE Transactions on Evolutionary Computation, 14(6): 959 - 974, Dec 2010
Abstract: In the real world, many optimization problems are dynamic. This requires an optimization algorithm to not only find the global optimal solution under a specific environment but also to track the trajectory of the changing optima over dynamic environments. To address this requirement, this paper investigates a clustering particle swarm optimizer (PSO) for dynamic optimization problems. This algorithm employs a hierarchical clustering method to locate and track multiple peaks. A fast local search method is also introduced to search optimal solutions in a promising subregion found by the clustering method. Experimental study is conducted based on the moving peaks benchmark to test the performance of the clustering PSO in comparison with several state-of-the-art algorithms from the literature. The experimental results show the efficiency of the clustering PSO for locating and tracking multiple optima in dynamic environments in comparison with other particle swarm optimization models based on the multiswarm method.
Description: This article is posted here with permission from the IEEE - Copyright @ 2010 IEEE
URI: http://bura.brunel.ac.uk/handle/2438/5817
DOI: http://dx.doi.org/10.1109/TEVC.2010.2046667
ISSN: 1089-778X
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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