Multi-sub-swarm particle swarm optimization algorithm for multimodal function optimization

This paper presents a novel multi-sub-swarm particle swarm optimization (PSO) algorithm. The proposed algorithm can effectively imitate a natural ecosystem, in which the different sub-populations can compete with each other. After competing, the winner will continue to explore the original district, while the loser will be obliged to explore another district. Four benchmark multimodal functions of varying difficulty are used as test functions. The experimental results show that the proposed method has a stronger adaptive ability and a better performance for complicated multimodal functions with respect to other methods.