International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 2, No. 2, 29 Feb. 2012
Cover page and Table of Contents: PDF (size: 134KB)
Improved Particle Swarm Optimization for Constrained Optimization
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Author(s)
Zhicheng Qu
1
Qin Yang
1,*
Index Terms
Particle swarm optimization (PSO), evolutionary computation, constranined optimization
Abstract
In this paper, we present an improved particle swarm optimization (PSO) algorithm to solve constrained optimization problems. The proposed approach, called MPSO, employs a novel mutation operator to enhance the global search ability of PSO. In order to deal with constrains, MPSO uses mean violations mechanism and boundaries search. Simulation results on five famous benchmark problems show that MPSO achieves better results than standard PSO and another variant of PSO.
Cite This Paper
Zhicheng Qu,Qin Yang,"Improved Particle Swarm Optimization for Constrained Optimization", IJEME, vol.2, no.2, pp.21-28, 2012. DOI: 10.5815/ijeme.2012.02.04
Reference
[1]Z. Michalewicz and G. Nazhiyath, “Genocop III: A co-evolutionary algorithm for numerical optimization with nonlinar constraints,” Proceedings of the Second IEEE International Conference on Evolutionary Compuation, IEEE Press, 1995, pp. 647-651.
[2]K. Deb, “An efficient constraint handling method for genetic algorhtms,” Computer Methods in Applied Mechanics and Enginnering, vol. 186, no. 2/4, pp. 311-338, 2000.
[3]T. P. Runarsson and X. Yao, “Stochastic ranking for constrained evolutionary optimization,” IEEE Transactions on Evolutionary Compuation, vol. 4, no. 3, pp. 284-294, 2000.
[4]E. Mezura-Montes and C. A. C. Coello, “A simple multimembered evolution strategy to solve constrained optimization problems,” IEEE Transactions on Evolutionary Compuation, vol . 9, no. 1, pp.. 1-17, 2005.
[5]A. H. Aguirre, S. B. Rionda, C. A. C. Coello, G. L. Lizarrga and E. M. Montes, “Handling constraints using multiobjective optimization concepts,” International Journal for Numerical Methods in Engineering, vol. 59, no. 15, pp. 1989-2017, 2004.
[6]J. Kennedy and R. C. Eberhart, “Particle swarm optimization”, Proceedings of IEEE International Conference on Neural Networks, 1998, pp. 1942–1948.
[7]Y. Shi, R.C. Eberhart, “A modified particle swarm optimizer”, Proceedings of the Conference on Evolutionary Computation, IEEE Press, Piscataway, 1998, pp. 69–73.
[8]J. J. Liang, T. P. Runarsson, E. Mezura-Montes, M. Clerc, N. Suganthan, C. A. C. Coello, and K. Deb, “Problem Definitions and Evaluation Criteria for the CEC 2006 Special Session on Constrained Real-Parameter Optimization,” Technical Report, No. 2006005, Nanyang Technological University, Singapore and et al., Dec, 2005.
[9]H. Lu and W. Chen, “Dynamic-objective particle swarm optimization for constrained optimization problems,” Journal of Combinatorial Optimization, vol. 12, pp. 409-419, 2006.