Considering the bad convergence and search efficiency of particle swarm optimization(PSO) when applied to
multi-dimensional optimization problem, an algorithm of adaptive chaotic variation particle swarm optimization(ACPSO)
is proposed after analyzing the convergence of PSO. The proposed algorithm uses the technique of double subpopulation
cooperative evolution. One subpopulation uses a new disturbed extremum method of chaotic mutation to improve the global
convergence ability, and the other uses the adaptive inertia weight and neighborhood information to enhance the local search
ability. The simulation results of four typical functions show that the proposed algorithm can attain better optimization
performance.