摘要： 粒子群优化算法存在早熟收敛和搜索精度较低的问题。为此，提出一种基于自适应混沌粒子群的优化算法。采用自适应权重和遗传算法中的交叉、变异操作更新粒子群，增加种群粒子的多样性，运用早熟判断机制判断粒子的当前状态，当粒子处于早熟状态时，利用混沌搜索的方法引导群体快速跳出局部最优。仿真结果表明，该算法可以有效解决粒子群算法的早熟问题，提高搜索精度和收敛速度。

关键词: 目标分配, 遗传算法, 自适应权重, 混沌, 粒子群优化算法

Abstract: An algorithm based on adaptive chaotic Particle Swarm Optimization(PSO) is put forward to overcome the problem of the premature convergence and low precision of PSO algorithm. This algorithm mixes adaptive weight, selection and variation operation of genetic algorithm and chaos algorithm. This can increase the species diversity of particles. The method of judging the local convergence is used to judge the particles statement. When the particles are in local convergence, the chaotic search method is used to guide the group out of local optima. Simulation results show that this algorithm not only can solve the local convergence problem effectively, but also can speed up the convergence rate and improve the search precision.

Key words: target distribution, Genetic Algorithm(GA), adaptive weight, chaos, Particle Swarm Optimization(PSO) algorithm

中图分类号: 

• TP301.6