文献类型：期刊文献

英文题名：Research on an improved genetic algorithm for logistics distribution path optimization

作者：Sun, Xue[1,2]; Yang, Chih-Kuang[3]; Wei, Kai-Cheng[3]; Wu, Chao-Chin[3]; Chen, Liang-Rui[2]

第一作者：Sun, Xue;孙雪

通讯作者：Wu, Chao-Chin

机构：[1] College of Urban Rail Transit and Logistics, Beijing Union University, Beijing, China; [2] Department of Electrical Engineering, National Changhua University of Education, Changhua, Taiwan; [3] Department of Computer Science and Information Engineering, National Changhua University of Education, Changhua, Taiwan

第一机构：北京联合大学城市轨道交通与物流学院

年份：2018

卷号：Part F148260

起止页码：282-286

外文期刊名：ACM International Conference Proceeding Series

收录：EI(收录号：20192407021487)

语种：英文

外文关键词：Computational efficiency - Computational complexity - Traveling salesman problem - Heuristic algorithms

摘要：Logistics distribution path optimization as an NP-hard problem is one of the important problems in logistics. Many intelligent algorithms are considered to be used to solve such problems. In this paper, an improved genetic algorithm is proposed for solving Travelling Salesman Problem (TSP), a kind of classical logistics distribution path optimization problem. The method uses island model genetic algorithm to formulate rules that are more suitable for TSP, through applying greedy algorithm in the generation of initial population, modifying the selection method and discussing different migration strategies, the computation time of solving the problem is shorten, the calculation efficiency is improved, and the probability of falling into the local optimal solution is reduced. Finally, experiments are conducted to discuss the effectiveness of our method. ? 2018 Association for Computing Machinery.