铁路行车调度问题的改进粒子群优化研究  

An Improved Particle Swarm Optimization Algorithm for Vehicle Routing Problem of Railway Transportation

作　　者：韩广 李雪杨 孙晓云 贾学梅 张超 聂怡凡 

机构地区：[1]石家庄铁道大学电气与电子工程学院,石家庄050043

出　　处：《控制工程》2017年第9期1855-1859,共5页Control Engineering of China

基　　金：国家自然科学基金(51274144)

摘　　要：针对车辆路径问题(Vehicle Routing Problem,简称VRP)的特点提出了一种改进的粒子群(Particle Swarm Optimization,简称PSO)算法。通过引入变异机制和改进惯性权重解决了标准PSO算法存在的初期收敛过慢、后期优化精度不高的问题。仿真实验表明,改进的PSO算法解决基本的8城市优化问题的收敛速度快,寻优精度高,优于相同条件下的双种群遗传算法、离散PSO算法;同时将改进的PSO算法应用于河北省11个市之间铁路运输的路径规划问题中,有效地规划了行车路径,节省了运输成本。For the vehicle routing problem （VRP）, an improved particle swarm optimization （PSO） algorithm is proposed. The improved PSO algorithm with variation mechanism and improved inertia weight can solve the problems of slow convergence speed and low accuracy in late optimization period. The simulation results show that the improved PSO algorithm increases the convergence speed and optimization accuracy compared with the double population GA and discrete PSO algorithm in the same condition for the basic VRP of 8 cities. What＇ more, the improved PSO algorithm is applied to VRP for the railway transportation among 11 cites in Hebei province. It effectively plans the traffic path and saves transportation costs.

关 键 词：车辆路径问题 粒子群优化 变异机制 惯性权重 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]