求解CVRP问题的混合遗传微粒群算法  

Hybrid Genetic Particle Swarm Optimization for CVRP

作　　者：李剑[1] 

机构地区：[1]湖北第二师范学院计算机工程系,武汉430205

出　　处：《计算机与数字工程》2009年第11期21-24,67,共5页Computer & Digital Engineering

摘　　要：采用借鉴遗传算法的编码、交叉和变异操作的遗传微粒群算法对带车辆能力约束的车辆路径优化问题进行求解。设计了符合微粒群算法进化机制的变异算子和改进顺序交叉算子以满足遗传微粒群算法中三条染色体交叉与变异的需要。对多个基准测试实例仿真计算表明算法有效且具有收敛速度快和精度高的优点。The genetic particle swarm optimization which is derived from particle swarm optimization （PSO） and incorporated with genetic coding, crossover and mutation operators was employed to solve capacitated vehicle routing problem （CVRP）. The crossover and the mutation operators were employed based on the mechanisms of traditional PSO. The operators were implemented to perform the crossover and the mutation among the three chromosomes. The simulation resuits have shown the proposed approach was effective and with the merit viz., fast convergence and high precision.

关 键 词：微粒群算法 车辆路径优化 遗传算法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]