关键词: 车辆路径问题 改进粒子群优化算法 随机惯性权重

Abstract:

The Vehicle Routing Problem is a NP complete problem and is also a hot topic in the operational research field. Many people do research on it, but sea rching effiency and the rate of success are low and the cost is high. In view of this,the paper adopts the second-order oscillating particle swarm optim  ization, particle swarm optimization-randomly varying inertia weight,particle swarm optimization mutation operator, and simulated annealing particle swa rm optimization to solve the vehicle routing problem with time window. We list the differences when solving the problem through simulation experiments a  nd compare the four algorithms with the genetic algorithm, standard particle swarm optimization used in literature[14]. We can conclude that all the fou   r algorithms used in this article can decrease the cost, shorten the running time, and improve the rate of success effectively,and particle swarm optimi zation-randomly varying inertia weight is the best.

Key words: vehicle routing problem, improved particle swarm optimization, randomly varying inertia weight