关键词: 粒子群优化, 自适应策略, 混沌映射, 数值优化

Abstract: Basic particle swarm optimization algorithm has fewer parameters and fast optimization speed, but it suffers some drawbacks such as low optimization efficiency and falling easily into local optimal point in the optimization process. To improve the global optimization performance and local optimization efficiency of particle swarm optimization algorithm, this paper proposes a chaotic particle swarm optimization algorithm with adaptive mutation （ACPSO） by introducing a chaotic mapping and an adaptive strategy. Compared to five existing algorithms, numerical results on six benchmark test functions indicate that ACPSO algorithm has better performance.

Key words: particle swarm optimization, adaptive strategy, chaotic map, numerical optimization