基于粒子群算法的地铁列车节能运行优化

针对降低轨道交通运行能耗的问题,提出基于粒子群算法的地铁列车节能运行两阶段双层优化方法。首先依据列车行车组织的特点,建立列车运行调整模型,在第一阶段利用粒子群算法全面搜索列车最优节能驾驶曲线,即在符合运行时间约束的前提下获得最优运行曲线;第二阶段为时刻表运行时间优化,利用第一阶段所获得的优化结果,生成各区间的能耗-时间曲线,进而优化列车时刻表。采用某地铁2号线数据对此方法进行测试。测试结果表明:本优化算法对实际线路中列车的节能运行以及时刻表的制定与优化具有良好效果。

Optimization of Energy Saving Operation for Metro Trains Based on Particle Swarm Optimization

With an aim to reduce the energy consumption in rail transit system, an optimized bi-level two-phase method has thus been put forward to achieve an energy saving operation for metro trains based on Particle Swarm Optimization. A new model for train operation adjustment has been established based on an analysis of the characteristics of train operation organization. In the first phase, Particle Swarm Optimization (PSO) has been used to comprehensively search the energy-saving operation curves of metro trains, thus obtaining the optimum operation curve under the premise of accordant running time constraints. In the second phase, the energy consumption-time curve for each interval is to be generated based on the optimization results produced in the first phase so as to further optimize the train schedule. The model has been simulated by using the data of a Metro Line 2. Experimental results show that the improved algorithm outperforms the traditional algorithm in that it presents a better effect on energy-saving operation and the formulation and optimization of time table in actual train lines.