关键词: 粒子群算法, 全局优化, 自适应, 多种群

Abstract: A Particle Swarm Optimization based on Self-adaptive Multi-Swarm（PSO-SMS） algorithm is proposed to balance the exploration ability and development ability of the algorithm and improve its comprehensive performance on different problems. It consists of three modules, including the recombination, adjustment of sub-swarm size and detection. In the initial stage of evolution, the entire swarm is divided into many sub-swarms. The recombination module enables the different sub-swarms to share advantageous information, which is beneficial to the optimization of uni-modal and multi-modal functions. When the swarm is trapped in a potential local optimum, the detection module can help the swarm jump out of the current local optimum based on certain historical information from the search process. Through the adjustment to sub-swarm size, the size of each sub-swarm gradually increases during evolution, which will facilitate the improvement of exploration ability in the initial stage and the later development ability of the algorithm. The comparison between CEC2013 test suite and other seven PSO algorithms shows that the PSO-SMS algorithm has outstanding performance in solving the optimization problems of different functions.

Key words: particle swarm optimization, global optimization, self-adaptive, multi-swarm