Multi-swarm Particle Swarm Optimization with a Center Learning Strategy

This paper proposes a new variant of particle swarm optimizers, called multi-swarm particle swarm optimization with a center learning strategy (MPSOCL). MPSOCL uses a center learning probability to select the center position or the prior best position found so far as the exemplar within each swarm. In MPSOCL, Each particle updates its velocity according to the experience of the best performing particle of its partner swarm and its own swarm or the center position of its own swarm. Experiments are conducted on five test functions to compare with some variants of the PSO. Comparative results on five benchmark functions demonstrate that MPSOCL achieves better performances in both the optimum achieved and convergence performance than other algorithms generally.

[1]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[2]  Konstantinos E. Parsopoulos UPSO : A Unified Particle Swarm Optimization Scheme , 2004 .

[3]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[4]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[5]  Ben Niu,et al.  An Improved MCPSO with Center Communication , 2008, 2008 International Conference on Computational Intelligence and Security.

[6]  Kalyan Veeramachaneni,et al.  Fitness-distance-ratio based particle swarm optimization , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[7]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[8]  José Neves,et al.  The fully informed particle swarm: simpler, maybe better , 2004, IEEE Transactions on Evolutionary Computation.

[9]  J. Kennedy,et al.  Population structure and particle swarm performance , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[10]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[11]  Q. Henry Wu,et al.  MCPSO: A multi-swarm cooperative particle swarm optimizer , 2007, Appl. Math. Comput..