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CLC number: N941; TP301.6

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Received: 2005-11-23

Revision Accepted: 2006-02-26

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Journal of Zhejiang University SCIENCE A 2006 Vol.7 No.12 P.1989~1994

http://doi.org/10.1631/jzus.2006.A1989


Identification of strategy parameters for particle swarm optimizer through Taguchi method


Author(s):  KHOSLA Arun, KUMAR Shakti, AGGARWAL K.K.

Affiliation(s):  Department of Electronics and Communication Engineering, National Institute of Technology, Jalandhar 144011, India; more

Corresponding email(s):   khoslaak@nitj.ac.in, shakti@hec.ac.in, kka@ipu.edu

Key Words:  Strategy parameters, Particle swarm optimization (PSO), Taguchi method, ANOVA


KHOSLA Arun, KUMAR Shakti, AGGARWAL K.K.. Identification of strategy parameters for particle swarm optimizer through Taguchi method[J]. Journal of Zhejiang University Science A, 2006, 7(12): 1989~1994.

@article{title="Identification of strategy parameters for particle swarm optimizer through Taguchi method",
author="KHOSLA Arun, KUMAR Shakti, AGGARWAL K.K.",
journal="Journal of Zhejiang University Science A",
volume="7",
number="12",
pages="1989~1994",
year="2006",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2006.A1989"
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%A KUMAR Shakti
%A AGGARWAL K.K.
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DOI - 10.1631/jzus.2006.A1989


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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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[7] Ross, P.J., 1996. Taguchi Techniques for Quality Engineering. McGraw Hill.

[8] Scheffe, H., 1999. The Analysis of Variance. Wiley Interscience Publication, John Wiley and Sons.

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[11] Shi, Y., Eberhart, R.C., 2001. Fuzzy Adaptive Particle Swarm Optimization. Proceedings of Congress on Evolutionary Computation, p.101-106.

[12] Taguchi, G., Chowdhury, S., Wu, Y., 2005. Taguchi Quality Engineering Handbook. John Wiley and Sons.

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[14] Xie, X.F., Zhang, W.J., Yang, Z.L., 2002. Adaptive Particle Swarm Optimization on Individual Level. International Conference on Signal Processing (ICSP 2002), p.1215-1218.

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