| A Self-Learning Particle Swarm Optimizer for Global Optimization Problems. | |
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MedLine Citation:
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PMID: 22067435 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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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. |
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Authors:
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Changhe Li; Shengxiang Yang; Trung Thanh Nguyen |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-11-04 |
Journal Detail:
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Title: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society Volume: - ISSN: 1941-0492 ISO Abbreviation: - Publication Date: 2011 Nov |
Date Detail:
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Created Date: 2011-11-9 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9890044 Medline TA: IEEE Trans Syst Man Cybern B Cybern Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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