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dc.contributor.authorLi, Changheen
dc.contributor.authorYang, Shengxiangen
dc.date.accessioned2013-06-13T12:59:22Z
dc.date.available2013-06-13T12:59:22Z
dc.date.issued2008
dc.identifier.citationLi, C. and Yang, S. (2008) Fast multi-swarm optimatization for dynamic optimization problems. In: Proceedings of the 4th International Conference on Natural Computation, ICNC '08. Jinan, China, October 2008. Vol. 7. New York: IEEE, pp. 624-628.en
dc.identifier.isbn9780769533049
dc.identifier.urihttp://hdl.handle.net/2086/8738
dc.description.abstractIn the real world, many applications are non-stationary optimization problems. This requires that the optimization algorithms need to not only find the global optimal solution but also track the trajectory of the changing global best solution in a dynamic environment. To achieve this, this paper proposes a multi-swarm algorithm based on fast particle swarm optimization for dynamic optimization problems. The algorithm employs a mechanism to track multiple peaks by preventing overcrowding at a peak and a fast particle swarm optimization algorithm as a local search method to find the near optimal solutions in a local promising region in the search space. The moving peaks benchmark function is used to test the performance of the proposed algorithm. The numerical experimental results show the efficiency of the proposed algorithm for dynamic optimization problems.en
dc.language.isoenen
dc.publisherIEEE.en
dc.subjectDynamic optimization problemen
dc.subjectMoving peak benchmark functionen
dc.subjectMultiswarm optimizationen
dc.subjectParticle swarm optimizationen
dc.subjectSearch methoden
dc.titleFast multi-swarm optimatization for dynamic optimization problems.en
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1109/ICNC.2008.313
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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