This paper proposes an improved multi-swarm cooperative particle swarm optimizer with center communication (MCPSO-CC) based on our previous proposed MCPSO algorithm, which enhances the particles based on the experience of master swarm and slave swarms. In our original MCPSO, there is no information sharing among slave swarms except that the information of the best performing particle is broadcasted to the master swarm. To deal with this issue in MCPSO-CC the population is divided into several identical sub-swarms and a center communication strategy is used to transfer the information among all the sub-swarms. To demonstrate the efficiency of the proposed MCPSO-CC algorithm, its performance is compared with SPSO and MCPSO on four well-know benchmark functions. Experimental results show that MCPSO-CC achieves not only better solutions but also faster convergence.
Riccardo Poli,et al.
Particle swarm optimization
Jing J. Liang,et al.
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation.
Konstantinos E. Parsopoulos,et al.
UPSO: A Unified Particle Swarm Optimization Scheme
International Conference of Computational Methods in Sciences and Engineering 2004 (ICCMSE 2004).
Kalyan Veeramachaneni,et al.
Fitness-distance-ratio based particle swarm optimization
Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).
Q. Henry Wu,et al.
MCPSO: A multi-swarm cooperative particle swarm optimizer
Appl. Math. Comput..
Michael N. Vrahatis,et al.
Unified particle swarm optimization for tackling operations research problems
Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..
Russell C. Eberhart,et al.
A new optimizer using particle swarm theory
MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.