Presentation 2011-06-09
Particle Swarm Optimization Considering Component Combined with Personal Best Positions
Ryosuke KUBOTA, Masashi HIRAKAWA, Hakaru TAMUKOH,
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Abstract(in English) Particle swarm optimization (PSO), which is included in swarm intelligence, is a population-based stochastic optimization technique. The motivation of the PSO is based on social behavior of fish schooling or bird flocking. The PSO is attractive due to the simplicity of its concept and the facility for the applications to diverse optimization problems. However, the traditional PSO can not work in high dimensional searching space effectively. In this paper, we propose a new PSO considering a combined component, which is obtained from the personal best positions of the particles in the searching space. The present PSO first calculates a center of the personal best positions weighted by their fitness values, and uses it for the update of the particle position. The effectiveness and the validity of the proposed PSO are verified by applying it to the some benchmarks of the continuous variable optimization problems.
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Keyword(in English) Swarm intelligence / particle swarm optimization (PSO) / personal best position / continuous variable optimization problem
Paper # SIS2011-1
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Conference Date 2011/6/2(1days)
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Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Particle Swarm Optimization Considering Component Combined with Personal Best Positions
Sub Title (in English)
Keyword(1) Swarm intelligence
Keyword(2) particle swarm optimization (PSO)
Keyword(3) personal best position
Keyword(4) continuous variable optimization problem
1st Author's Name Ryosuke KUBOTA
1st Author's Affiliation Department of Intelligent System Engineering, Ube National College of Technology()
2nd Author's Name Masashi HIRAKAWA
2nd Author's Affiliation Faculty of Informatics, Kansai University
3rd Author's Name Hakaru TAMUKOH
3rd Author's Affiliation Institute of Engineering, Tokyo University of Agriculture and Technology
Date 2011-06-09
Paper # SIS2011-1
Volume (vol) vol.111
Number (no) 78
Page pp.pp.-
#Pages 4
Date of Issue