A Combined Local Best Particle Swarm Optimization Algorithm


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This paper proposes a combined local best particle swarm optimization algorithm (CLBPSO) which combined with local optimum particle information. And it gives three ways of combination local information. Experimental results indicate that the CLBPSO algorithm improves the search performance on the benchmark functions significantly. On the basis of experimental results, we will also compare these three methods with each other to find the best one.



Edited by:

Prasad Yarlagadda and Yun-Hae Kim




Z. G. Lian et al., "A Combined Local Best Particle Swarm Optimization Algorithm", Applied Mechanics and Materials, Vols. 333-335, pp. 1388-1391, 2013

Online since:

July 2013




[1] Kennedy J. Stereotyping: improving particle swarm performance with cluster analysis. In: Proceedings of the IEEE International Conference on Evolutionary Computation[C], 2000: 1507-1512.

[2] Kennedy J and Eberhart R.C. A discrete binary version of the particle Swarm algorithm. In: Proc. Int. Conf. Syst. ManCybern[C], Orlando, FL, 1997: 4104-4108.

[3] Qi Kang, Lei Wang, Derong Liu and Qidi Wu. Parameter Approximate Dynamic Optimization for PSO Systems. Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference Shanghai, P.R. China, December 16-18, 2009. 5003-5008.

DOI: https://doi.org/10.1109/cdc.2009.5400841

[4] Wei-Der Chang, Shun-Peng Shi. PID controller design of nonlinear systems using an improved particle swarm optimization approach. In: Nonlinear Science and Numerical Simulation [J], November 2010, pp: 3632-3639.

DOI: https://doi.org/10.1016/j.cnsns.2010.01.005

[5] Swapna Devi, Devidas G. Jadhav and Shyam S. Pattnaik. PSO Based Memetic Algorithm for Unimodal and Multimodal Function Optimization. B.K. Panigrahi et al. (Eds. ): SEMCCO 2011, Part I, LNCS 7076, pp.127-134, (2011).

DOI: https://doi.org/10.1007/978-3-642-27172-4_16