高德地图及改进ACA在车辆路径寻优中的应用  

Application of Amap and Improved Ant Colony Algorithm in Vehicle Routing Optimization

作　　者：刘庆华 汪晶 LIU Qinghua;WANG Jing(School of Computer,Jiangsu University of Science and Technology,Zhenjiang 212000)

机构地区：[1]江苏科技大学计算机学院,镇江212000

出　　处：《计算机与数字工程》2020年第6期1354-1359,1432,共7页Computer & Digital Engineering

基　　金：国家863计划项目(编号:2013AA12A206);国家自然科学基金项目(编号:51008143)资助。

摘　　要：车辆路径优化问题研究中,通常采用目标节点之间的线性距离之和最短作为最优解的求解结果。但在实际道路中,节点之间存在诸多因素,使得通过采用传统求解方式得出来的最优路径结果难以运用于实际场景。由此论文提出了结合高德地图API和改进的蚁群算法(ACA)来将理论运用于实际情形,首先通过高德提供的对应API接口来获取各个节点之间的实际道路距离,然后对基本的蚁群算法在在选择策略,信息素挥发系数ρ和计算目标节点距离等方面进行改进,最后将通过高德地图获取的实际节点之间的距离让改进后的蚁群算法进行最优路径求解。通过实验结果分析表明,论文提出的方法很好地将理论与实际情况相结合,具有高度的可行性和实用性。In the research of vehicle path optimization problem,the sum of the linear distances between target node is usually used as the solution result of the optimal solution. However,in the actual road,there are many factors between the nodes,so that the optimal path results obtained by using the traditional solution method are difficult to apply to the actual scene. Therefore,this paper proposes to apply the theory to the actual situation by combining the Amap API and the improved ant colony algorithm. Firstly,the actual road distance between each node is obtained through the corresponding API interface provided by Amap,and then the basic ant colony algorithm improves the selection strategy,pheromone volatilization coefficient of ρ and calculation of node distance.Finally,the improved ant colony algorithm is used to solve the optimal path by the distance between the actual nodes obtained through the Amap. The experimental results show that the proposed method combines the theory with the actual situation and has high feasibility and practicability.

关 键 词：路径优化 高德地图API 改进蚁群算法 目标节点 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]