摘要
为提高物流配送中车辆路径的寻优效率,提高物流经济效益,介绍一种能够有效求解VRP问题的算法—改进的混合型蚁群算法。该算法在近邻法构造初始解的基础上,使用2-opt局部搜索策略对当前得到的最优解和次优解进行改进,在更新全局信息素时采用基于排序的蚂蚁系统对排在前2名的蚂蚁更新全局信息素,且为全局信息素设置最大值和最小值。使用Matlab仿真工具对N44K6等10个经典VRP问题进行了求解,得到的结果和已知最优解的误差很小,都在6%以下,并且N33K6问题得到了和已知最优解相同的解。与基本蚁群算法得到的解进行了比较,证明了该改进算法的有效性。
In order to improve the efficiency of vehicle routing optimization in logistics and distribution, to improve the economic efficiency of logistics, an effective algorithm, a improved mixed ant colony algorithm, to solve the vehicle routing problem（VRP） is introduced. The algorithm is based on the traditional ant colony system algorithm, at the beginning an initial result is constructed using the nearest neighbor method, and then the result is improved using 2-opt partial search strategy for the best result and the second best result of the iteration, and then the global pheromone for the best two colonies is upda- ted, the pheromone of which must have a maximum constraint and a minimum constraint. The maximum and the minimum constraint is related to the best result of the iteration. The classic N34K6 and other nine VRP problems are simulated using the MATLAB tool, the results of which have small deviations compared to the best-known results. All the deviations are all smaller than 6 percent. And the result same as the best-known result for N33K6 is obtained. Compared the results with ones of the base ant colony optimiza- tion algorithm, it is found that the proposed mixed ant colony algorithm is a better one to solve the VRP.
出处
《黑龙江大学自然科学学报》
CAS
北大核心
2014年第3期328-334,共7页
Journal of Natural Science of Heilongjiang University
基金
国家自然科学基金资助项目(61275120)
