Opposition based comprehensive learning particle swarm optimization

Abstract

This paper proposes a novel scheme that we call the opposition based comprehensive learning particle swarm optimizers (OCLPSO), which employs opposition based learning (OBL) for population initialization and also for exemplar selecting. This scheme enables the swarm to explore and exploit with the more diversity and not to be premature convergence. Experiments were conducted on benchmark functions and comparisons between the original CLPSO and the OCLPSO are presented. The results are very promising, as the OCLPSO seems to find better solutions in multimodal problems when compared with the CLPSO.