基于改进遗传算法的生鲜农产品物流配送路径优化  

A Improved Genetic Algorithm for Vehicle Routing Problem with Fresh Food Delivery

作　　者：陈鑫影[1] 李依琳 肖司义 CHEN Xinying;LI Yilin;XIAO Siyi(School of Computer and Communication Engineering,Dalian Jiaotong University,Dalian 116028,China)

机构地区：[1]大连交通大学计算机与通信工程学院,辽宁大连116028

出　　处：《大连交通大学学报》2022年第5期97-102,111,共7页Journal of Dalian Jiaotong University

基　　金：辽宁省科技计划资助项目(1655706734383);辽宁省自然科学基金资助项目(2019-ZD-0105)。

摘　　要：为解决生鲜农产品配送路径优化问题,提出了一种改进型烟花遗传算法IFWGA.算法融合遗传算法与烟花算法,为弥补遗传算法早熟收敛的缺陷,对每代遗传算法的最优解和最差解执行烟花算法.同时,为兼顾全局搜索能力和局部搜索能力,算法中设置了两种步长的动态变异算子.根据迭代次数控制变异算子动态切换,加快算法的收敛速度,提高算法的搜索精度.仿真实验结果表明:所提出的IFWGA算法的收敛速度较快,变异算子设置较为合理,求解质量较高.Adding the cold chain transportation cost to the mathematical model of the traditional vehicle routing problem,the fresh agricultural products distribution routing optimization problem is formed.Although fusion algorithm is adopted to solve the problem,but there are still problems such as poor solution quality,premature convergence.In order to solve these problems,an improved fireworks genetic algorithm IFWGA is proposed by combining genetic algorithm with fireworks algorithm.In order to compensate for the precocious convergence defect of genetic algorithm,the fireworks algorithm is implemented for the optimal and worst solutions of each generation of the genetic algorithm.At the same time,two step sizes of dynamic mutation operators are set in the algorithm in order to take into account the global search ability and local search ability.The mutation operators can be dynamically switched according to the number of iterations,so as to speed up the convergence speed and improve the search accuracy of the algorithm.The simulation results show that the IFWGA algorithm proposed in this paper has fast convergence speed,reasonable mutation operators and high solution quality.

关 键 词：车辆路径问题 烟花算法 遗传算法 冷链物流 

分 类 号：F307[经济管理—产业经济] TP18[自动化与计算机技术—控制理论与控制工程]