Summary

Proceedings of the 2013 International Symposium on Nonlinear Theory and its Applications

2013

Session Number:B1L-B

Session:

Number:201

A hybrid Algorithm based on Particle Swarm Optimization and Differential Evolution for Global Optimization Problems

Jun-ichi Kushida,  Akira Hara,  Tetsuyuki Takahama,  

pp.201-204

Publication Date:

Online ISSN:2188-5079

DOI:10.15248/proc.2.201

PDF download (259KB)

Particle swarm optimization (PSO) and differential evolution (DE) are the evolutionary algorithms, and both have been successfully applied to various optimization problems. In this paper, we propose a hybrid algorithm based on PSO and DE. The design of the proposed method is mainly constructed from the algorithm of PSO, and DE operator is used supplementarily. In order to preserve diversity of population and have a rotation-invariant, we modify velocity update equation and add perturbation by difference vector. In addition, we introduce the uneven movement of particles, by updating position of some particles in the swarm. The proposed method was compared with basic PSO and DE. The simulation results showed the effectiveness of the proposed method on several test functions.

[1] Kennedy, J. and Eberhart, R. “Particle Swarm Optimization,” in Proc of the IEEE International Conference on on Neural Networks , vol. 4, pp. 1942-1948, 1995.

[2] Storn, R. and Price, K. “Differential evolution - a simple and effcient adaptive scheme for global optimization over continuous spaces,”Technical Report TR-95-012, 1995.

[3] Z. F. Hao, G. H. Guo and H. Huang, “A Particle Swarm Optimization Algorithm with Differential Evolution,”in Proc. the Sixth International Conference on Machine Learning and Cybernetics, pp. 1031-1035, 2007.

[4] W. J. Zhang and X. F. Xie, “DEPSO: Hybrid Particle Swarm with Differential Evolution Operator,” in Proc. IEEE International Conference on Systems, Man and Cybernetics, pp. 3816-3821, 2003.

[5] M. G. H. Omran, A. P. Engelbrecht and A. Salman, “Differential Evolution Based Particle Swarm Optimization,” in Proc. the 2007 IEEE Swarm IntelligenceSymposium, pp. 112-119, 2007.

[6] Clerc, M. and Kennedy, J., “The particle swarm - explosion, stability, and convergence in a multidimensional complex space,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, pp. 58-73, 2002.

[7] T. Takahama and S. Sakai, “Efficient Nonlinear Optimization by Differential Evolution with a Rotation-Invariant Local Sampling Operation,” in Proc. of 2011 IEEE Congress on Evolutionary Computation(CEC2011), pp.2215-2222, 2011.