改进的蚁群算法求解动态车辆路径问题OACHSSCD
Improved Ant Colony Algorithm for Solving Dynamic Vehicle Routing Problems
针对带时间窗的动态车辆路径问题,以最小化车辆数为首要目标、最小化总行驶距离为次要目标,提出一种改进蚁群算法.首先,利用动态客户点构建动态最小生成树,更新蚂蚁路径上的信息素.然后,结合交换、插入等多种邻域算子,设计自适应选择算子策略,对最优蚂蚁路径进行局部优化,以增强算法的全局搜索和局部优化能力.最后,基于 48 个公开测试案例,本算法与并行蚁群算法相比,在 45 个案例上取得更好解;与二分图匹配蚁群算法相比,在39个案例上取得更好解,表明本算法在求解动态车辆路径问题时具有高效性.
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.
王鸿禹;陈仕军
湖北文理学院 数学与统计学院,湖北 襄阳 441053湖北文理学院 数学与统计学院,湖北 襄阳 441053
信息技术与安全科学
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
dynamic vehicle routing problemtime windowsdynamic minimum spanning treeneighborhood operatorsadaptive selection operator strategy
《湖北文理学院学报》 2026 (8)
72-79,8
国家自然科学基金项目(71501064)湖北省自然科学基金计划项目(2026AFB537)
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