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中文摘要：
针对粒子群优化（particle swarm optimization，PSO）算法易陷入早熟的缺陷，提出了一种基于自适应惯性权重的混沌粒子群算法。首先利用立方映射产生的混沌序列对粒子位置进行初始化，为全局搜索的多样性奠定基础；然后采用自适应惯性权重优化策略，提高收敛速度；最后如果判断算法陷入早熟，则对算法进行混沌扰动，使其跳出局部最优。仿真实验结果表明，改进算法的收敛速度及收敛精度都有明显提高，能有效地避免早熟。
英文摘要：
Aiming at the premature convergence problem which the particle swarm optimization algorithm suffers from,a chaos particle swarm optimization based on adaptive inertia weight is proposed. Firstly, chaotic sequence generated by cube map is used to initiate individual position, which strengthens the diversity of global searching. Secondly, adaptive inertia weight is adopted to improve the convergence rate. Furthermore, chaos perturbation is utilized to avoid the prema- ture convergence. The results of the simulation experiment show that the convergence rate and the precision of the im- proved algorithm are obviously enhanced, and the algorithm can effectively avoid the premature convergence problem.
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