Cooperative particle swarm optimization with ICS and Its application to parameter identification of PMSM

In this paper, a cooperative particle swarm optimization algorithm using the immune clonal selection(ICS) is proposed, named CPSO-ICS. The proposed algorithm coupled with a memory scheme and multiple population. The best individual of each normal subpopulation will be selected into the memory, during the search process. The improved immune clonal selection operator is employed for optimizing the memory population while the migration scheme is employed for the communication between memory population and normal subpopulations. The performance of the proposed algorithm is tested on some standard benchmark functions, which shows a faster convergence and global search ability. Finally, the CPSO-ICS is applied to the parameter estimation of permanent magnet synchronous machines, which shows that its performance is significantly outperforms other PSOs.