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dc.contributor.authorLi, C-
dc.contributor.authorYang, S-
dc.identifier.citation4th International Conference on Natural Computation, Jinan, 7: 624 - 628, 18 -20 Oct 2008en_US
dc.descriptionThis article is posted here with permission of IEEE - Copyright @ 2008 IEEEen_US
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_US
dc.subjectApplication softwareen_US
dc.subjectBenchmark testingen_US
dc.subjectComputer scienceen_US
dc.subjectEvolutionary computationen_US
dc.subjectParticle swarm optimizationen_US
dc.subjectParticle trackingen_US
dc.subjectSearch methodsen_US
dc.titleFast multi-swarm optimization for dynamic optimization problemsen_US
dc.typeConference Paperen_US
pubs.organisational-data/Brunel/Brunel (Active)-
pubs.organisational-data/Brunel/Brunel (Active)/School of Info. Systems, Comp & Maths-
pubs.organisational-data/Brunel/Research Centres (RG)-
pubs.organisational-data/Brunel/Research Centres (RG)/CIKM-
pubs.organisational-data/Brunel/School of Information Systems, Computing and Mathematics (RG)-
pubs.organisational-data/Brunel/School of Information Systems, Computing and Mathematics (RG)/CIKM-
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Computer Science
Dept of Computer Science Research Papers

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