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dc.contributor.authorYang, Shengxiangen
dc.date.accessioned2013-05-15T12:44:39Z
dc.date.available2013-05-15T12:44:39Z
dc.date.issued2012
dc.identifier.citationWang, H., Moon, I.-K. and Yang, S. (2012) A memetic particle swarm optimization algorithm for multimodal optimization problems. Information Sciences, 197, August 2012, pp. 38-52.en
dc.identifier.issn0020-0255
dc.identifier.urihttp://hdl.handle.net/2086/8469
dc.description.abstractRecently, multimodal optimization problems (MMOPs) have gained a lot of attention from the evolutionary algorithm (EA) community since many real-world applications are MMOPs and may require EAs to present multiple optimal solutions. In this paper, a memetic algorithm that hybridizes particle swarm optimization (PSO) with a local search (LS) technique, called memetic PSO (MPSO), is proposed for locating multiple global and local optimal solutions in the fitness landscape of MMOPs. Within the framework of the proposed MPSO algorithm, a local PSO model, where the particles adaptively form different species based on their indices in the population to search for different sub-regions in the fitness landscape in parallel, is used for globally rough exploration, and an adaptive LS method, which employs two different LS operators in a cooperative way, is proposed for locally refining exploitation. In addition, a triggered re-initialization scheme, where a species is re-initialized once converged, is introduced into the MPSO algorithm in order to enhance its performance of solving MMOPs. Based on a set of benchmark functions, experiments are carried out to investigate the performance of the MPSO algorithm in comparison with some EAs taken from the literature. The experimental results show the efficiency of the MPSO algorithm for solving MMOPs.en
dc.language.isoenen
dc.publisherElsevier B.V.en
dc.subjectMultimodal optimization problemen
dc.subjectMemetic algorithmen
dc.subjectParticle swarm optimizationen
dc.subjectLocal searchen
dc.subjectSpeciesen
dc.titleA memetic particle swarm optimization algorithm for multimodal optimization problems.en
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1016/j.ins.2012.02.016
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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