基于统计步长PSO算法的烟草配送线路优化方案研究  

Research on the Optimization of Tobacco Distribution Route Based on Statistical Step PSO Algorithm

作　　者：钱漫 钟培泉 张亚萍 张振军 QIAN Man;ZHONG Peiquan;ZHANG Yaping;ZHANG Zhenjun(Guangdong Tobacco DongGuan Company Ltd.,Dongguan Guangdong 523000)

机构地区：[1]广东烟草东莞市有限公司,广东东莞523000

出　　处：《河南科技》2021年第9期28-30,共3页Henan Science and Technology

摘　　要：烟草配送线路优化问题是烟草行业物流层面的重点问题之一。微粒群算法(Particle Swarm Optimization,简称PSO)是一种利用群体智能的随机全局优化方法,广泛应用于路径寻优、函数优化等领域。为了提高PSO算法的全局搜索能力,本文在标准PSO算法的基础上,定义了一种基于统计步长的微粒群算法:针对PSO算法易出现局部极值问题,引入惯性权重因子进行改进;针对PSO算法中加速步长为常量值而不符合实际情况的问题,定义了基于统计的加速步长进化方程。实践证明,本文研究的算法应用于烟草配送线路优化方案中,可以大大节约运输里程及运输成本。The optimization of tobacco distribution routes is one of the key issues at the logistics level of the tobacco industry.Particle swarm optimization(PSO) is a stochastic global optimization method using swarm intelligence,which is widely used in route optimization,function optimization and other fields.In order to improve the global search ability of PSO algorithm,based on the standard PSO algorithm,the paper defined a particle swarm optimization algorithm based on statistical step size:aiming at improving the global search ability of PSO algorithm,the inertia weight factor was defined;aiming at solving the problem that the acceleration step size in PSO algorithm was constant,the evolution equation of acceleration step size based on statistics was defined.The practice shows that the algorithm studied in this paper can greatly save the transportation mileage and transportation cost when it is applied to the tobacco distribution route optimization model.

关 键 词：线路优化 微粒群算法(PSO) 烟草配送线路优化方案 

分 类 号：F252[经济管理—国民经济]