Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorLi, C-
dc.contributor.authorYang, S-
dc.contributor.authorNguyen, T T-
dc.identifier.citationIEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, Forthcoming 2011en_US
dc.descriptionCopyright @ 2011 IEEE. All Rights Reserved. This article was made available through the Brunel Open Access Publishing Fund.en_US
dc.description.abstractParticle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means that all particles in a swarm use the same strategy. This monotonic learning pattern may cause the lack of intelligence for a particular particle, which makes it unable to deal with different complex situations. This paper presents a novel algorithm, called self-learning particle swarm optimizer (SLPSO), for global optimization problems. In SLPSO, each particle has a set of four strategies to cope with different situations in the search space. The cooperation of the four strategies is implemented by an adaptive learning framework at the individual level, which can enable a particle to choose the optimal strategy according to its own local fitness landscape. The experimental study on a set of 45 test functions and two real-world problems show that SLPSO has a superior performance in comparison with several other peer algorithms.en_US
dc.description.sponsorshipThis work was supported by the Engineering and Physical Sciences Research Council of U.K. under Grants EP/E060722/1 and EP/E060722/2.en_US
dc.subjectGlobal optimization problemen_US
dc.subjectOperator adaptationen_US
dc.subjectParticle swarm optimization (PSO)en_US
dc.subjectSelf-learning particle swarm optimizer (SLPSO) ,en_US
dc.subjectTopology adaptationen_US
dc.titleA self-learning particle swarm optimizer for global optimization problemsen_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-
Appears in Collections:Computer Science
Brunel OA Publishing Fund
Dept of Computer Science Research Papers

Files in This Item:
File Description SizeFormat 
Fulltext.pdf528.45 kBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.