改进蚁群算法在智能交通中的应用  

Application of Improved Ant Colony Optimization in Intelligent Transportation

作　　者：宋方[1] 汪镭[1] 

机构地区：[1]同济大学电子与信息工程学院,上海201804

出　　处：《数学的实践与认识》2013年第3期66-72,共7页Mathematics in Practice and Theory

基　　金：教育部博士点基金(20100072110038);教育部新世纪人才计划项目;国家自然科学基金项目(70871091,61075064,61034004,61005090)

摘　　要：为了诱导车辆在出行时选择较高质量的路线,提出并建立了城市道路权值仿真模型.为求解该模型,从分析基本蚁群算法入手,通过在状态转移规则中加入扰动因子,改进全局更新规则,以及引入信息素更新算子改进了蚁群算法.然后利用道路权值模型对两种算法在路径寻优效果上做了比较和分析,实验结果表明改进后的蚁群算法能有效地避免停留在局部最优解,并提高计算效率,具有良好的寻优性和收敛性,能准确找出路网中满足综合要求的最优路径.In order to guide vehicles to portation network, an urban road weights plan better travel routes for users in urban trans- model is proposed and established. Then, to solve this model, based on the ant colony optimization, global update rules of the ant colony are re-designed, a disturbance factor is added, and a pheromone update operator is introduced. As a result, an improved ant colony optimization is generated. The urban road weights model is used in analyzing and comparing the effects of road planning by two algorithms. The ex- perimental results show that improved ant colony optimization, which can effectively avoid staying in the local optimal solution and improve computational efficiency, is of good opti- mization ability and rapid convergence, and it can accurately find the optimal road meeting multiple requirements.

关 键 词：智能交通 最优路径 蚁群算法 道路权值模型 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP399-C6[自动化与计算机技术—控制科学与工程]