湖北文理学院学报2026，Vol.47Issue(8)：72-79,8.
改进的蚁群算法求解动态车辆路径问题
Improved Ant Colony Algorithm for Solving Dynamic Vehicle Routing Problems
摘要
Abstract
For the dynamic vehicle routing problem with time windows,aiming at minimizing the number of vehicles as the primary objective and minimizing the total travel distance as the secondary objective,an improved ant colony algorithm is proposed.Firstly,dynamic customer points are used to construct a dynamic minimum spanning tree,so as to update the pheromone on ant paths.Then,combined with multiple neighborhood operators such as exchange and insertion,an adaptive selection operator strategy is designed to perform local optimization on the optimal ant paths,thereby enhancing the global search and local optimization capabilities of the algorithm.Finally,48 public test cases are solved and compared with advanced algorithms in the literature.Compared with the parallel ant colony algorithm,the proposed algorithm achieves better solutions in 45 cases;compared with the bipartite graph matching ant colony algorithm,it achieves better solutions in 39 cases.The experimental results show that the proposed algorithm is efficient in solving the dynamic vehicle routing problem.关键词
动态车辆路径问题/时间窗/动态最小生成树/邻域算子/自适应选择算子策略Key words
dynamic vehicle routing problem/time windows/dynamic minimum spanning tree/neighborhood operators/adaptive selection operator strategy分类
信息技术与安全科学引用本文复制引用
王鸿禹,陈仕军..改进的蚁群算法求解动态车辆路径问题[J].湖北文理学院学报,2026,47(8):72-79,8.基金项目
国家自然科学基金项目(71501064) （71501064）
湖北省自然科学基金计划项目(2026AFB537) （2026AFB537）
