Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33370
Title: A memetic particle swarm optimisation algorithm for dynamic multi-modal optimisation problems
Authors: Wang, H
Yang, S
Ip, WH 
Wang, D
Keywords: dynamic multi-modal optimisation problem
local search
memetic algorithm
memetic computing
particle swarm optimisation
speciation
Issue Date: 2012
Publisher: Taylor & Francis Ltd
Source: International journal of systems science, 2012, v. 43, no. 7, p. 1268-1283 How to cite?
Journal: International Journal of Systems Science 
Abstract: Many real-world optimisation problems are both dynamic and multi-modal, which require an optimisation algorithm not only to find as many optima under a specific environment as possible, but also to track their moving trajectory over dynamic environments. To address this requirement, this article investigates a memetic computing approach based on particle swarm optimisation for dynamic multi-modal optimisation problems (DMMOPs). Within the framework of the proposed algorithm, a new speciation method is employed to locate and track multiple peaks and an adaptive local search method is also hybridised to accelerate the exploitation of species generated by the speciation method. In addition, a memory-based re-initialisation scheme is introduced into the proposed algorithm in order to further enhance its performance in dynamic multi-modal environments. Based on the moving peaks benchmark problems, experiments are carried out to investigate the performance of the proposed algorithm in comparison with several state-of-the-art algorithms taken from the literature. The experimental results show the efficiency of the proposed algorithm for DMMOPs.
URI: http://hdl.handle.net/10397/33370
DOI: 10.1080/00207721.2011.605966
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