基于自适应量子人工鱼群算法的动态路径诱导研究

发布时间：2019-05-20 05:29

【摘要】：智能交通系统作为国际上公认的解决城市交通拥堵问题最经济有效的办法,受到社会各界的广泛关注。动态路径诱导系统作为智能交通系统的关键技术之一,在城市道路交通流的均衡动态分配中起着举足轻重的作用。本文正是基于这样的应用背景,针对动态路径诱导系统的核心组成部分：动态路径诱导算法进行了基于自适应量子人工鱼群算法的研究。文章内容主要包括以下两个部分：动态路网模型的建立以及动态路径诱导算法的设计。第一部分：对城市交通路网进行数学抽象描述以后,针对城市交通路网的特点和动态路网的不足,建立了带路段转向信息和实时交通流信息的动态路网模型,并利用基于地图颜色聚类特征的道路提取方法,提取沈阳市和平区部分电子地图的道路骨架作为构建城市交通路网的参考对象,建立了用来进行动态路径诱导问题求解的城市交通路网拓扑图。第二部分：动态路径诱导算法是动态路径诱导系统求解最优路径的关键,因此所设计算法性能的优劣将直接关系到整个系统的实时性和有效性。依据动态路径诱导算法应满足的要求选取基本人工鱼群算法作为动态路径诱导算法的基础研究对象,并从提高全局寻优能力、提高寻优精度和改善迭代计算效率方面对基本人工鱼群算法进行了改进,提出了一种自适应量子人工鱼群算法。最后给出了利用自适应量子人工鱼群算法求解动态路径诱导问题的执行步骤。最终实验结果表明：利用自适应量子人工鱼群算法来求解带有路段转向信息和实时交通流信息的动态最优路径是可行有效的,且在求解效率和求解精度方面均要优于基本的人工鱼群算法和传统的遗传算法。
[Abstract]:As the most economical and effective way to solve the problem of urban traffic congestion, intelligent transportation system has been widely concerned by all walks of life. As one of the key technologies of intelligent transportation system, dynamic path guidance system plays an important role in the balanced dynamic distribution of urban road traffic flow. Based on this application background, this paper studies the dynamic path induction algorithm, which is the core component of dynamic path induction system, based on adaptive quantum artificial fish swarm algorithm. The content of this paper mainly includes the following two parts: the establishment of dynamic road network model and the design of dynamic path induction algorithm. The first part: after the mathematical abstract description of the urban traffic network, according to the characteristics of the urban traffic network and the shortcomings of the dynamic road network, a dynamic road network model with road section steering information and real-time traffic flow information is established. The road skeleton of some electronic maps in Heping District of Shenyang is extracted by using the road extraction method based on the color clustering features of the map, which is used as the reference object for the construction of the urban traffic network. The topological diagram of urban traffic network is established to solve the dynamic path guidance problem. The second part: the dynamic path induction algorithm is the key to solve the optimal path of the dynamic path induction system, so the performance of the designed algorithm will be directly related to the real-time and effectiveness of the whole system. According to the requirements that the dynamic path induction algorithm should meet, the basic artificial fish swarm algorithm is selected as the basic research object of the dynamic path induction algorithm, and the global optimization ability is improved. In order to improve the optimization accuracy and iterative computational efficiency, the basic artificial fish swarm algorithm is improved, and an adaptive quantum artificial fish swarm algorithm is proposed. Finally, the execution steps of using adaptive quantum artificial fish swarm algorithm to solve the dynamic path induction problem are given. The experimental results show that it is feasible and effective to solve the dynamic optimal path with road steering information and real-time traffic flow information by using adaptive quantum artificial fish swarm algorithm. It is superior to the basic artificial fish swarm algorithm and the traditional genetic algorithm in solving efficiency and accuracy.
【学位授予单位】：东北大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U495;TP18

【相似文献】