摘要
设计了一种求解随机需求车辆路径问题的改进的粒子群优化(PSO)算法.在算法后期将变异算子引入PSO算法,克服了基本PSO算法易陷入局部最优的缺点.数值模拟结果表明改进的PSO算法提高了全局搜索能力,求解效果优于基本PSO算法和遗传算法.
This paper studies the vehicle routing problem with stochastic demands(VRPSD), and devises an advanced PSO to solve this problem. By adding the mutation operator to the PSO algorithm in the later phase, the advanced PSO algorithm overcome the basic PSO's shortcoming of easily plunging into the local optimization. Experimental results indicate that the advanced PSO algorithm can improve the global convergence ability and is superior to basic PSO algorithm and genetic algorithm.
出处
《武汉大学学报(理学版)》
CAS
CSCD
北大核心
2007年第1期41-44,共4页
Journal of Wuhan University:Natural Science Edition
基金
国家自然科学基金资助项目(A0324666
70471034)
关键词
随机
车辆路径问题
变异算子
粒子群优化算法
stochastic
vehicle routing problem
mutation operator
particle swarm optimization