蚁群算法在城市交通路径选择中的应用

针对城市交通路径选择问题,引入蚁群算法并将其改进为可同时满足对路程和时间最优的路径搜索算法,设计了相关的搜索规则和流程.在大量试验的基础上,讨论了算法中各种参数对路径搜索算法收敛性(包括收敛速度和准确度)的影响,并获得了一纽最优的经验参数.分析了搜索中产生伪最优解路径的规律,并通过控制收敛速度和加快趋向最优路径对蚁群算法进行了优化.结果显示,所进行的优化能有效抑制伪最优路径的产生,在2个周期内即可完成搜索.

Urban Vehicle Routing Based on Ant Colony Algorithm

To solve the problem of urban traffic vehicle routing, an improved ant colony system (ACS) algorithm was proposed. The algorithm focuses on both distance and time costs of path planning. Its search rules and flow charts were given. Based on extensive simulation results, the effects of parameters of the algorithm on the convergence performance, including convergence rate and convergence accuracy, were discussed, and a set of empirical parameters were obtained. The reasons for fake optimal paths involved in the search were analyzed. Further, the algorithm was optimized by controlling the convergence rate and forcing the convergence toward to the optimal path. The simulation result indicates that the optimization is effective in restraining fake optimal paths and has a convergence rate within 2 cycles per search.