Co-evolutionary particle swarm optimization algorithm based on elite immune clonal selection

A novel Elite immune clonal selection co-evolutionary particle swarm optimization algorithm (named, EICS-CPSO) is proposed based on the elite strategy and co-evolutionary mechanism. The algorithm is consisting of one elite subpopulation and several normal subpopulations based on collaborative computing frame. The elite individuals having high fitness from each normal subpopulation will be selected into the elite subpopulation, during the evolution process. The elite subpopulation will be promoted by the immune clonal selection operator with adaptive wavelet mutation. Furthermore, a simple Cauchy learning operator is utilized for accelerating the convergence speed of the pbest particles while the migration scheme is employed for the information exchange between elite subpopulation and normal subpopulations. The performance of the proposed algorithm is verified through a suite of standard benchmark functions, which shows a faster convergence and global search ability and also has a good dynamic optimization performance. Moreover, the parameters of the EICS-CPSO are analyzed in experiments and the results show that EICS-CPSO is insensitive to parameters and easy to use.