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基于智能优化算法的动态路径诱导方法研究进展 被引量:2

New Trends of Dynamic Route Guidance Methods Based on Intelligent Optimization Algorithms
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摘要 采用综述的方法对当前动态路径诱导方法中一些有代表性的智能优化算法进行了深刻的探讨与总结,为未来进行深入而广泛的智能交通系统研究及应用奠定基础。主要从算法特性、改进效果、性能评价等方面详细讨论了智能优化算法在动态路径诱导系统中的常见改进机制及其效果,给出了这些优化算法的基本思想、优缺点及其应用局限性;并对智能优化算法性能评价方法的研究现状进行了详细的分析与总结,为建模人员和研究人员对智能交通系统中动态路径诱导方法的选择和研究提供支持;最后结合算法应用分析成果,展望了智能优化算法在动态路径诱导系统中的应用发展前景和智能交通系统中进一步研究未来动态路径诱导算法的重要研究方向。 Some representative intelligent optimization algorithms in the dynamic route guidance meth?ods were discussed and summed up by the review method, which laid a foundation for the future researchin the intelligent transportation system deeply and widely. The improvement mechanism and the applica?tion results of the intelligent optimization algorithm are analyzed from the view of the algorithm character?istics, improvement effect, performance evaluation, etc. And the basic idea, advantages, disadvantagesand limitations of these algorithms were given. Besides, the research status of evaluation methods of theintelligent optimization algorithm performance was analyzed, which helped engineers and researchers toselect the most suitable variability modeling techniques. Finally, combining with the analysis results ofalgorithms application, the application prospect and some important research directions in the future fur?ther research of the intelligent optimization algorithms in intelligent transportation system were forecast.
作者 游尧 林培群
出处 《交通运输研究》 2015年第1期20-26,共7页 Transport Research
基金 国家自然科学基金项目(51108191) 广东省自然科学基金项目(S2013010013871)
关键词 动态路径诱导方法 研究进展 智能优化算法 蚁群算法 遗传算法 dynamic route guidance methods research progress intelligent optimization algorithms ant colony optimization genetic algorithm
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  • 1李威武,王慧,钱积新.智能交通系统中路径诱导算法研究进展[J].浙江大学学报(工学版),2005,39(6):819-825. 被引量:33
  • 2黄席越,张著洪,何传江,等.现代智能算法理论及应用[M].北京:科学出版社,2005.
  • 3钟-文,杨建刚.智能优化方法及其应用研究[D].杭州:浙江大学,2005.
  • 4Schoonderwoerd R, Holland O, Bruten J, et al. Ant-BasedLoad Balancing in Telecommunications Networks[J]. Adap-tive Behavior, 1996, 5(2): 169-207.
  • 5Colorni A, Dorigo M, Maniezzo V, et al. Ant System for Job-Shop Scheduling[J]. Belgian J of Operations Research Statis-tics and Computer Science, 1994, 34(1): 39-53.
  • 6Dorigo M, Maniezzo V, Colorni A. Ant System: Optimizationby a Colony of Cooperating Agents[J]. IEEE Transactions onSystem, Man, and Cybernetics-Part B, 1996, 26(1): 29-41.
  • 7Dorigo M, Gambardella L M. Ant Colony System: A Coopera-tive Learning Approach to the Traveling Salesman Problem[J]. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 53-66.
  • 8Mohemmed A W, Sahoo N C, Geok T K. Solving ShortestPath Problem Using Particle Swarm Optimization[J]. AppliedSoft Computing, 2008, 8(4): 1643-1653.
  • 9杜长海,黄席樾,杨祖元,唐明霞,杨芳勋.改进的蚁群算法在动态路径诱导中的应用研究[J].计算机工程与应用,2008,44(27):236-239. 被引量:10
  • 10YANG Jian- ren. A Dynamic Route Guidance AlgorithmBased on Modified Ant Colony Optimization[C]// ComputerNetwork and Multimedia Technology. Wuhan: IEEE, 2009:1-3.

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