A self-learning particle swarm optimizer for global optimization problems

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dc.contributor.author Li, Changhe en
dc.contributor.author Yang, Shengxiang en
dc.contributor.author Nguyen, T. T. en
dc.date.accessioned 2012-08-09T15:02:01Z
dc.date.available 2012-08-09T15:02:01Z
dc.date.issued 2012-06
dc.identifier.citation Li, C., Yang, S. and Nguyen, T.T. (2012) A self-learning particle swarm optimizer for global optimization problems. IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, 42 (3), pp 627-646 en
dc.identifier.issn 1083-4419
dc.identifier.uri http://hdl.handle.net/2086/6772
dc.description.abstract Particle 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
dc.language.iso en en
dc.subject global optimization problem en
dc.subject operator adaptation en
dc.subject particle swarm optimization (PSO) en
dc.subject self-learning particle swarm optimizer (SLPSO) en
dc.subject topology adaptation en
dc.title A self-learning particle swarm optimizer for global optimization problems en
dc.type Article en
dc.identifier.doi http://dx.doi.org/10.1109/TSMCB.2011.2171946
dc.researchgroup Centre for Computational Intelligence en
dc.peerreviewed Yes en


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