Presentation 2007-03-16
Evolutionary Design of Particle Swarm Optimization Using Real-Coded Genetic Algorithm
Hong ZHANG, Masumi ISHIKAWA,
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Abstract(in English) Particle Swarm Optimization (PSO) is a stochastic and population-based search algorithm that demonstrates its effctiveness in solving complex nonlinear optimization problems. Although the original PSO is very simple and effective, how to determine appropriate values of parameters in PSO is yet to be found. This paper proposes a novel method called evolutionary PSO, which estimates values of parameters in PSO for effectively finding globally optimal parameter values by a real-coded genetic algorithm. A crucial idea here is to adopt a temporary cumulative fitness instead of instantaneous fitness in a real-coded genetic algorithm for evaluating the performance of the PSO. It provides a useful measure that efficiently determines appropriate values of parameters in PSO. To demonstrate the effectiveness of the proposed method, we implement a simple computer experiment on a 2-dimensional optimization problem, and analyze the characteristics of dependency on initial condition.
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Keyword(in English) particle swarm optimization / real-coded genetic algorithm / elitism strategy / estimation
Paper # NC2006-198
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Committee NC
Conference Date 2007/3/9(1days)
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Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Evolutionary Design of Particle Swarm Optimization Using Real-Coded Genetic Algorithm
Sub Title (in English)
Keyword(1) particle swarm optimization
Keyword(2) real-coded genetic algorithm
Keyword(3) elitism strategy
Keyword(4) estimation
1st Author's Name Hong ZHANG
1st Author's Affiliation Graduate School of Life Science & Systems Engineering Kyushu Institute of Technology()
2nd Author's Name Masumi ISHIKAWA
2nd Author's Affiliation Graduate School of Life Science & Systems Engineering Kyushu Institute of Technology
Date 2007-03-16
Paper # NC2006-198
Volume (vol) vol.106
Number (no) 590
Page pp.pp.-
#Pages 6
Date of Issue