求解农业物流配送中心选址的自学习蝗虫算法  

Self-learning grasshopper algorithm for solving location problem of agricultural logistics distribution center

作　　者：李静 陶娟 LI Jing;TAO Juan(College of Computer and Information Engineering,Guizhou University of Commerce,Guiyang 550004,China;College of Mathematics and Statistics,Guizhou University,Guiyang 550025,China)

机构地区：[1]贵州商学院计算机与信息工程学院,贵州贵阳550004 [2]贵州大学数学与统计学院,贵州贵阳550025

出　　处：《计算机工程与设计》2023年第6期1749-1757,共9页Computer Engineering and Design

基　　金：贵州省教育厅自然科学基金项目(黔教合KY字[2017]022)。

摘　　要：为降低物流配送成本,提高配送效率,提出自学习蝗虫优化算法IGOA求解农业物流配送中心选址问题。为提高标准GOA算法的寻优精度和速率,设计佳点集种群初始化方法,提高种群分布均匀性;引入伪对立学习对最优个体变异,增强种群跳离局部最优的能力;根据适应度对种群对半划分,分别对两个子种群进行配对自学习和邻域搜索,提高种群寻优质量。利用改进蝗虫优化算法IGOA对农业物流配送中心选址模型迭代寻优,其结果表明,IGOA算法可以降低物流配送成本,提高配送效率。To reduce the distribution cost and improve the efficiency of logistics distribution,a self-learning grasshopper optimization algorithm IGOA was proposed to solve the location problem of agricultural logistics distribution center.To improve the optimization accuracy and speed of standard GOA,a population initialization method based on good point set was designed to enhance the uniformity of the initial population distribution.The pseudo opposition learning was used to perturb the optimal individual,so that the population had the ability to jump away from the local optimum.The population was divided in half according to the fitness,the paired self-learning mechanism and the neighborhood search mechanism were designed for two sub populations to improve the quality of population optimization.IGOA was used to optimize the location model of agricultural logistics distribution center.The results show IGOA can reduce the logistics distribution cost and improve the distribution efficiency.

关 键 词：物流配送中心选址 蝗虫优化算法 佳点集 伪对立学习 邻域搜索 自学习 配送效率 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]