中文摘要：

通过结合混沌的遍历性和粒子群的快速性的优点，提出了一种用于求解物流配送路径优化问题的混沌粒子群优化算法。该算法利用混沌变量产生初始粒子群，对子代部分粒子群进行微小扰动，随着搜索过程深入逐步调整扰动幅度，通过调整惯性权重因子克服标准PSO算法的早熟和易陷入局部最优值等缺陷。将混沌粒子群优化算法用于物流配送路径优化，建立了数学模型，在此基础上设计了相应的算法。将该算法和遗传算法、标准粒子群算法进行比较，证明了其收敛速度和寻优能力的优越性。

英文摘要：

Combining traverse of chaos and quickness of particle swarm,a chaotic particle swarm optimization algorithm is proposed for logistics distribution route problem.This algorithm generates the initial particles and adds a small disturbance to the partial particles of child generation group by using chaos variable.The disturbance amplitude is adjusted little by little and adjusts the inertia weighting factor as the search goes on to break away from local best solutions.This algorithm of cha- otic particle swarm optimization is investigated to solve logistics distribution route problem.The mathematic mode is established and the solution algorithm is developed.The simulation results of example indicate that chaotic particle swarm optimization algorithm solves the defects of genetic of Genetic Algorithm（GA） and the PSO algorithm which are apt to trap in local minimums and premature problem,and has great advantage of convergence property.