Aiming at the two characteristics of premature convergence of particle swarm optimization that the particle velocity approaches 0 and particle swarm congregate, this paper learns from the annealing function of the simulated annealing algorithm and adaptively and dynamically adjusts inertia weights according to the velocity information of particles to avoid approaching 0 untimely. This paper uses the good uniformity of Anderson chaotic mapping and performs chaos perturbation to part of particles based on the information of variance of the population's fitness to avoid the untimely aggregation of particle swarm. The numerical simulations of five test functions are performed and the results are compared with several swarm intelligence heuristic algorithms. The results shows that the modified algorithm can keep the population diversity well in the middle stage of the iterative process and it can improve the mean best of the algorithm and the success rate of search.

Aiming at the drawback of the local search ability of weakness of adaptive chaos particle swarm optimization (ACPSO), which is based on the variance of population's fitness, this paper presents that introduces the chaos mutation and chaos search to the ACPSO. By using the An chaos mapping to proceed the chaos mutation for some particles and take chaos search for the global optimal particle, it proposes a rule which takes into account the positions of particles and adaptive mutative scale of optimizing space. The results of numerical simulation show that the convergence, and the global and local search ability of the new method are improved, and can effectively avoid premature convergence.

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