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dc.contributor.authorYang, Shengxiangen
dc.identifier.citationWang, H. et al. (2012) A memetic particle swarm optimisation algorithm for dynamic multi-modal optimisation problems. International Journal of Systems Science, 43(7), July 2012, pp. 1268-1283.en
dc.description.abstractMany 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.en
dc.publisherTaylor & Francisen
dc.subjectMemetic computingen
dc.subjectMemetic algorithmen
dc.subjectParticle swarm optimisationen
dc.subjectDynamic multi-modal optimisation problemen
dc.subjectLocal searchen
dc.titleA memetic particle swarm optimisation algorithm for dynamic multi-modal optimisation problems.en
dc.researchgroupCentre for Computational Intelligenceen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en

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