Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/5994
Title: A self-learning particle swarm optimizer for global optimization problems
Authors: Li, C
Yang, S
Nguyen, T T
Keywords: Global optimization problem;Operator adaptation;Particle swarm optimization (PSO);Self-learning particle swarm optimizer (SLPSO) ,;Topology adaptation
Issue Date: 2011
Publisher: IEEE
Citation: IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, Forthcoming 2011
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.
Description: Copyright @ 2011 IEEE. All Rights Reserved. This article was made available through the Brunel Open Access Publishing Fund.
URI: http://bura.brunel.ac.uk/handle/2438/5994
DOI: http://dx.doi.org/10.1109/TSMCB.2011.2171946
ISSN: 1083-4419
Appears in Collections:Computer Science
Brunel OA Publishing Fund
Dept of Computer Science Research Papers

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