Abstract：Particle swarm optimization (PSO) is a new swarm intelligence algorithm.It has some advantages such as fewer parameters, easy implementation and good efficiency etc. such that obtains many applications. In order to improve the performance of PSO, based on adaptive particle swarm optimization and simulated annealing particle swarm optimization, a chaotic adaptive annealing particle swarm optimization based on chaotic mapping is proposed.A chaotic perturbation operator is added to near-optimal solutions to enhance the global search capability. A double selection strategy is adopted instead of the traditional inertia factor, which not only makes the inertia factor change with the change of objective function, but also with the change of distance between the current position and the previous one of the particle.The effect of self and group experience in iteration is dynamically adjusted by a linear decreasing accelerating factor.The performance of the improved algorithm is verified by numerical experiments. The results show that the improved algorithm is superior to adaptive PSO and simulated annealing PSO for different types of functions.
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