基于改进遗传算法求解车辆路径问题

Zeng Yi
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引用次数: 2

摘要

物流配送车辆路线问题是近年来物流研究的热点问题。这是一个NP问题,很难得到一个满意的最优解。本文在传统遗传算法中引入了除加法算子和优秀个体记忆机制,使改进的遗传算法能够保留优秀个体并保持种群多样性。数值模拟结果表明,改进的遗传算法能够弥补遗传算法容易陷入局部最优解和收敛速度慢的缺陷。并有效地解决了物流配送车辆的路线问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Solving Vehicle Routing Problem Based on Improved Genetic Algorithm
In recent years, logistics distribution vehicle routing problem is a hot topic in logistics research. It is a NP problem and hard to get an optimal and satisfactory solution. The paper introduces removing-addition operator and excellent individual memory mechanism to the traditional genetic algorithm, so that the improved genetic algorithm can kee excellent individuals and maintain the population diversity The results of numerical simulation show that the improve genetic algorithm can make up the defects of the geneti algorithm easy to fall into local optimal solution and slow convergence speed, and effectively solve the logistics distribution vehicle routing problem.
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