基于改进蚁群算法的动态路径规划算法研究  

Research on Dynamic Path Planning Algorithm Based on Improved Ant Colony Algorithm

作　　者：张叶茂[1] 杨晓武[2] 

机构地区：[1]南宁职业技术学院机电工程学院,广西南宁530007 [2]中南大学信息科学与工程学院,湖南长沙410075

出　　处：《西部交通科技》2017年第3期89-93,共5页Western China Communications Science & Technology

基　　金：2016年度广西壮族自治区中青年教师基础能力提升项目(KY2016YB628)

摘　　要：针对交通导航最优路径问题,文章分析了传统蚁群算法的基本原理及其局限性,并为提高路径搜索效率,解决路径搜索初始阶段缺乏方向性、正反馈机制引发局部最优解的问题,提出了新的改进蚁群算法,通过信息素更新策略改进、启发因子改进和引入探索因子来寻求动态全局最优解。由MATLAB实验仿真可知,改进后的蚁群算法对比基本蚁群算法,在遍历相同道路网络节点时,其循环的平均路径长度和最短路径长度都较短,能够有效实现动态最短路径诱导,具有良好的收敛性和全局寻优性。In view of the optimal path problem of traffic navigation,this article analyzed the basic principle and limitations of traditional ant colony algorithm, then, in order to improve the path search efficiency and solve the problem of being lack of directionality at initial path search stage and the problem that the positive feedback mechanism causes the local optimum, it proposed a new improved ant colony algorithm,and then found the dynamic global optimal solution by pheromone updating strategy improvement, heuristic factor improvement and introducing the exploratory factor.As shown in MATLAB experiment simulation, compared to the basic ant colony algorithm, the cycling average path length and shortest path length of improved ant colony algorithm were shorter when traversing the same road network nodes,which can realize the dynamic shortest path induction effectively, thus it has better convergence and global optimization.

关 键 词：蚁群算法 路径规划 信息素 因子 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]