摘要
针对公交车辆调度路线优化问题,提出了采用遗传蚁群混合算法(GAA)求解。建立了一个受条件限制的多目标公交路线优化选择的数学模型,引入遗传变异的进化过程提高蚁群算法的寻优效率,并在种群随机搜索过程中引入最优决策更新和判断,改善了寻优性能、加速了收敛,使算法同时具有随机性和确定性。并给出了算法求解的具体步骤。通过算例结果对比,证明了该算法对优化公交车辆路线调度的可行性。
On account of route optimization of bus dispatching, it is proposed to use genetic ant algorithm （GAA） for solution. A mathematic model for multi-objective bus route optimization and selection under limited conditions is developed, introducing the evo- lutionary process of genetic variation to improve the optimization of ant colony algorithm and also the optimal decision updating and identification in the course of random search of colony to improve the optimization performance and speed up convergence, thus allowing the algorithm with randomicity and determinacy. As a result, the solving steps of the algorithm are given in details. Through comparison of the example results, the algorithm is proved to be feasible anti practical for route optimization of bus dispatching.
出处
《微计算机信息》
2009年第31期48-49,100,共3页
Control & Automation
基金
甘肃省自然科学基金研究项目
基金申请人:汤旻安
任恩恩等
项目名称:基于离散事件动态系统的特殊地形城市道路交通智能控制研究与仿真
基金颁发部门:甘肃省科技厅(0803RJZA020)
关键词
遗传蚁群算法
公交车辆调度
多目标优化
genetic algorithm-ant colony algorithin
bus dispatching
multi-objective opthnization
