Evolving Particle Swarm Optimization Implemented by a Genetic  Algorithm

Jenn-Long Liu

doi:10.20965/jaciii.2008.p0284

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JACIII Vol.12 No.3 pp. 284-289

doi: 10.20965/jaciii.2008.p0284

(2008)

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Evolving Particle Swarm Optimization Implemented by a Genetic Algorithm

Jenn-Long Liu

Department of Information Management, I-Shou University, 1, Section 1, Hsueh-Cheng Rd., Ta-Hsu Hsiang, Kaohsiung County, Taiwan 840, Taiwan

Received:

April 15, 2007

Accepted:

September 22, 2007

Published:

May 20, 2008

Keywords:

evolving PSO, genetic algorithm, cognitive and social learning rates

Abstract

Particle swarm optimization (PSO) is a promising evolutionary approach related to a particle moves over the search space with velocity, which is adjusted according to the flying experiences of the particle and its neighbors, and flies towards the better and better search area over the course of search process. Although the PSO is effective in solving the global optimization problems, there are some crucial user-input parameters, such as cognitive and social learning rates, affect the performance of algorithm since the search process of a PSO algorithm is nonlinear and complex. Consequently, a PSO with well-selected parameter settings may result in good performance. This work develops an evolving PSO based on the Clerc’s PSO to evaluate the fitness of objective function and a genetic algorithm (GA) to evolve the optimal design parameters to provide the usage of PSO. The crucial design parameters studied herein include the cognitive and social learning rates as well as constriction factor for the Clerc’s PSO. Several benchmarking cases are experimented to generalize a set of optimal parameters via the evolving PSO. Furthermore, the better parameters are applied to the engineering optimization of a pressure vessel design.

Cite this article as:

J. Liu, “Evolving Particle Swarm Optimization Implemented by a Genetic Algorithm,” J. Adv. Comput. Intell. Intell. Inform., Vol.12 No.3, pp. 284-289, 2008.

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References

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] Z. Michalewicz, “Genetic Algorithms + Data Structures = Evolution Programs,” Springer-Verlag, Berlin, 1996.

[2] [2] J. R. Koza, “Genetic Programming: On the Programming of Computers by Means of Natural Selection,” The MIT Press, Cambridge, MA, 1992.

[3] [3] L. J. Fogel, “Evolutionary Programming in Perspective: the Topdown View in Computational Intelligence,” Imitating Life, J. M. Zurada, R. J. Marks II, and C. J. Robinson (Eds.), IEEE Press, Piscataway, NJ, 1994.

[4] [4] I. Rechenberg, “Evolution Strategy, in Computational Intelligence,” Imitating Life, J. M. Zurada, R. J. Marks II, and C. J. Robinson (Eds.), IEEE Press, Piscataway, NJ, 1994.

[5] [5] M. Dorigo and T. Stützle, “Ant Colony Optimization,” The MIT Press, Cambridge, MA, 2004.

[6] [6] J. Kennedy, and R. C. Eberhart, “Particle Swarm Optimization,” Proc. IEEE Int. Conf. on Neural Networks (Perth Australia), IEEE service Center, Piscataway, NJ, IV, 1942-1948, 1995.

[7] [7] R. C. Eberhart and Y. Shi, “Comparison Between Genetic Algorithms and Particle Swarm Optimization,” Evolutionary Programming, V. P. Saravanan, N. Waagen, A. E. Eiben (Eds.), VII, Spinger Press, 611-616, 1998.

[8] [8] L. P. Zhang, H. J. Yu, and S. X. Hu, “Optimal Choice of Parameters for Particle Swarm Optimization,” J. of Zhejiang University SCIENCE, 6A-6, pp. 528-534, 2005.

[9] [9] A. Carlisle and G. Dozier, “An Off-the-shelf PSO,” Proc. of the Workshop on Particle Swarm Optimization, Indianapolis (IN), USA, 2001.

[10] [10] M. Clerc and J. Kennedy, “The Particle Swarm- Explosion, Stability, and Convergence in a Multidimensional Complex Space,” IEEE Trans. on Evolutionary Computation, Vol.6, No.1, pp. 58-73, 2002.

[11] [11] I. C. Trelea, “The Particle Swarm Optimization Algorithm: Convergence Analysis and Parameter Selection,” Information Processing Letters, Vol.85, pp.317-325, 2003.

[12] [12] Y. Shi and R. C. Eberhart, “Parameter Selection in Particle Swarm Optimization,” 1998 Annual Conf. on Evolutionary Programming, San Diego, Vo.7, pp. 591-600, 1998.

[13] [13] Y. Shi and R. C. Eberhart, “A Modified Particle Swarm Optimizer,” IEEE Int. Conf. on Evolutionary, Anchorage, AK, 1998.

[14] [14] S. Naka, T. Genji, Yura, and Y. Fukuyama, “Practical Distribution State Estimation Using Hybrid Particle Swarm Optimization,” Proc. of IEEE Power Engineering Society Winter Meeting, Columbus, Ohio, 2001.

[15] [15] M. Clerc, “The Swarm and the Queen: Towards a Deterministic and Adaptive Particle Swarm Optimization,” Proc. of 1999 ICEC, Washington, DC, pp. 1951-1957, 1999.

[16] [16] R.C. Eberhart and Y. Shi, “Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization,” Congress on Evolutionary Computing, Vol.1, pp. 84-88, 2000.

[17] [17] E. Sandgren, “Nonlinear Integer and Discrete Programming in Mechanical Design Optimization,” Trans. of the ASME, J. of Mechanical Design, Vol.112, No.2, pp. 223-229, 1990.