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dc.contributor.authorLi, C-
dc.contributor.authorYang, S-
dc.identifier.citationIEEE Congress on Evolutionary Computation (CEC 2009): 381 - 388, 2009-05-18 - 2009-05-21en_US
dc.descriptionThis article is posted here with permission of the IEEE - Copyright @ 2009 IEEEen_US
dc.description.abstractTraditional particle swarm optimization (PSO) suffers from the premature convergence problem, which usually results in PSO being trapped in local optima. This paper presents an adaptive learning PSO (ALPSO) based on a variant PSO learning strategy. In ALPSO, the learning mechanism of each particle is separated into three parts: its own historical best position, the closest neighbor and the global best one. By using this individual level adaptive technique, a particle can well guide its behavior of exploration and exploitation. A set of 21 test functions were used including un-rotated, rotated and composition functions to test the performance of ALPSO. From the comparison results over several variant PSO algorithms, ALPSO shows an outstanding performance on most test functions, especially the fast convergence characteristic.en_US
dc.description.sponsorshipThis work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of the United Kingdom under Grant EP/E060722/1.en_US
dc.subjectLearning (artificial intelligence)en_US
dc.subjectParticle swarm optimisationen_US
dc.titleAn adaptive learning particle swarm optimizer for function optimizationen_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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