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Received: 2004-02-10

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Journal of Zhejiang University SCIENCE A 2005 Vol.6 No.6 P.528~534

10.1631/jzus.2005.A0528


Optimal choice of parameters for particle swarm optimization


Author(s):  ZHANG Li-ping, YU Huan-jun, HU Shang-xu

Affiliation(s):  Department of Chemical Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   zhanglp@infotech.zju.edu.cn, yuhj@infotech.zju.edu.cn, sxhu@mail.hz.zj.cn

Key Words:  Particle swarm optimization (PSO), Constriction factor method (CFM), Parameter selection


ZHANG Li-ping, YU Huan-jun, HU Shang-xu. Optimal choice of parameters for particle swarm optimization[J]. Journal of Zhejiang University Science A, 2005, 6(6): 528~534.

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Abstract: 
The constriction factor method (CFM) is a new variation of the basic particle swarm optimization (PSO), which has relatively better convergent nature. The effects of the major parameters on CFM were systematically investigated based on some benchmark functions. The constriction factor, velocity constraint, and population size all have significant impact on the performance of CFM for PSO. The constriction factor and velocity constraint have optimal values in practical application, and improper choice of these factors will lead to bad results. Increasing population size can improve the solution quality, although the computing time will be longer. The characteristics of CFM parameters are described and guidelines for determining parameter values are given in this paper.

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

Reference

[1] Carlisle, A., Dozier, G., 2001. An Off-the-shelf PSO. Proceedings of the Workshop on Particle Swarm Optimization, p.1-6.

[2] Clerc, M., 1999. The Swarm and Queen: Towards A Deterministic and Adaptive Particle Swarm Optimization. Proceedings of the IEEE Congress on Evolutionary Computation, p.1951-1957.

[3] Clerc, M., Kennedy, J., 2002. The particle swarm(explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. on Evolutionary Computation, 6(1):58-73.

[4] Eberhart, R.C., Kennedy, J., 1995. A New Optimizer Using Particle Swarm Theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, p.39-43.

[5] Eberhart, R.C., Shi, Y., 2000. Comparing Inertia Weight and Constriction Factors in Particle Swarm Optimization. Proceedings of the IEEE Congress on Evolutionary Computation, San Diego, CA, p.84-88.

[6] Eberhart, R.C., Shi, Y., 2001. Particle Swarm Optimization: Developments, Applications and Resources. Proceedings of the IEEE Congress on Evolutionary Computation, Seoul, Korea, p.81-86.

[7] Eberhart, R.C., Simpson, P.K., Dobbins, R.W., 1996. Computational Intelligence PC Tools. Academic Press Professional. Boston, MA.

[8] El-Gallad, A.I., El-Hawary, M.E., Sallam, A.A., Kalas, A., 2002. Enhancing the Particle Swarm Optimizer via Proper Parameters Selection. Canadian Conference on Electrical and Computer Engineering, p.792-797.

[9] Kennedy, J., Eberhart, R.C., 1995. Particle Swarm Optimization. Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, p.1942-1948.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

suguna@No address<No mail>

2013-04-27 02:09:46

nice

Bandla Sreenivasa Rao@No address<sreenibandla@yahoo.com>

2011-07-20 18:21:49

Thank YOu

Richard Medina<erndres@hotmail.com>

2010-10-25 01:58:31

thanks for the article

Chawalsak@UTM<chaliaw_utm@hotmail.com>

2010-10-15 21:04:02

good idea

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