摘要
蚁群算法在求解车辆路径问题过程中存在搜索时间长、易于陷入局部最优解的问题。为此,设计并实现一种混合蚁群算法。引入变异算子增强算法的全局搜索能力,采用2-opt法优化阶段最优解的子路径。通过对信息素的挥发因子进行动态调整,从而有效控制信息量的变化速度。实例仿真结果表明,该算法具有较好的求解效率和寻优效果。
Ant Colony Algorithm（ACA） has some short-comings such as its slow computing speed, and it is easy to fall in a local optimal. Based on the idea of ACA, a hybrid optimization algorithm for solving Vehicle Routing Problem（VRP） is proposed. The algorithm expands the scope of solution space and improves the global ability of the algorithm by importing mutation operator, optimizes the stage optimal solution further by combining 2-opt, and controls the rate of change in pheromone by adjusting configuration of parameters dynamically. Example simulation results show that this algorithm can get optimal resolution of VRP effectively and quickly.
出处
《计算机工程》
CAS
CSCD
北大核心
2011年第24期190-192,共3页
Computer Engineering
基金
国家自然科学基金资助项目"复杂环境下动态车辆路径问题的建模与优化"(60842004)
关键词
车辆路径问题
混合蚁群算法
变异算子
线路改进
动态规划
Vehicle Routing Problem（VRP）
hybrid Ant Colony Algorithm（ACA）
mutation operator
line improvement
dynamic programming
