Identification of strategy parameters for particle swarm optimizer through Taguchi method

Abstract：Particle swarm optimization (PSO), like other evolutionary algorithms is a population-based stochastic algorithm inspired from the metaphor of social interaction in birds, insects, wasps, etc. It has been used for finding promising solutions in complex search space through the interaction of particles in a swarm. It is a well recognized fact that the performance of evolutionary algorithms to a great extent depends on the choice of appropriate strategy/operating parameters like population size,crossover rate, mutation rate, crossover operator, etc. Generally, these parameters are selected through hit and trial process, which is very unsystematic and requires rigorous experimentation. This paper proposes a systematic based on Taguchi method reasoning scheme for rapidly identifying the strategy parameters for the PSO algorithm. The Taguchi method is a robust design approach using fractional factorial design to study a large number of parameters with small number of experiments. Computer simulations have been performed on two benchmark functions-Rosenbrock function and Griewank function-to validate the approach. 作者： Author： KHOSLA Arun[1]  KUMAR Shakti[2]  AGGARWAL K.K.[3] 作者單位： Department of Electronics and Communication Engineering, National Institute of Technology, Jalandhar 144011, IndiaCentre for Advanced Technology, Haryana Engineering College, Jagadhari 135003, IndiaVice Chancellor, GGS Indraprastha University, Delhi 110006, India 期 刊： 浙江大學學報A(英文版)   ISTICEISCI Journal： JOURNAL OF ZHEJIANG UNIVERSITY SCIENCE A 年，卷(期)： 2006, 7(12) 分類號： N941 TP301.6 Keywords： Strategy parameters    Particle swarm optimization (PSO)    Taguchi method    ANOVA    機標分類號： TS9 TN3 機標關鍵詞： Taguchi method    evolutionary algorithms    fractional factorial design    robust design    search space 基金項目： Identification of strategy parameters for particle swarm optimizer through Taguchi method[期刊論文]  浙江大學學報A(英文版) --2006, 7(12)Particle swarm optimization (PSO), like other evolutionary algorithms is a population-based stochastic algorithm inspired from the metaphor of social interaction in birds, insects, wasps, etc. It has b...

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