改进的蚁群算法在动态路径诱导中的应用研究  

Study on application of improved ant colony algorithm in dynamic route guidance

作　　者：杜长海[1] 黄席樾[1] 杨祖元[1] 唐明霞[1] 杨芳勋[1] 

机构地区：[1]重庆大学自动化学院,重庆400044

出　　处：《计算机工程与应用》2008年第27期236-239,共4页Computer Engineering and Applications

基　　金：重庆市科委自然科学基金(No.2006BA6016)~~

摘　　要：针对基本蚁群算法收敛速度慢和易陷入局部最优的缺点,在对信息素和启发信息进行标准化以消除量纲和取值范围影响的基础上,提出带方向的信息素更新和混沌选择策略来改进蚁群算法。将路网节点间的相对位置信息引入信息素更新,以加快搜索速度;使用混沌扰动改进选择策略,以避免出现早熟停滞现象。并将其用于城市交通动态路径诱导的研究中,以重庆市渝中半岛的路网为实例计算以最短行程时间为目标的最优路径,结果表明该算法是有效、可行的,比基本蚁群算法具有更好的全局搜索能力。Due to the disadvantage of relatively slow convergence and local optimum of basic ant colony algorithm,based on eliminating the influence of the size and dimension of pheromone and heuristic information through standardized transformation, directed pheromone updating on and chaotic selection strategy are proposed to improve ant colony algorithm.Relative location information among nodes in road network is introduced in pheromone updating for higher searching speed,and chaos perturbation is used to improve selection strategy to avoid precocity and stagnation.Then the improved ant colony algorithm is applied in urban traffic dynamic route guidance.The road network of Chongqing Yuzhong Peninsula is taken as an example to calculate the optimal route based on the least travel time,and the experimental results show that this algorithm has much higher capacity of global optimization than basic ant colony algorithm and it is feasible and effective for optimal route choice.

关 键 词：智能交通系统 动态路径诱导 蚁群算法 最优路径 混沌扰动 

分 类 号：U491.1[交通运输工程—交通运输规划与管理]