基于遗传算法的追踪列车节能优化

为了研究追踪列车的节能优化操纵策略,提出了四显示固定闭塞系统下的列车静态速度约束条件和追踪列车动态速度约束条件.在此基础上,建立了以列车操纵手柄级位和工况转换点为控制变最的追踪列车节能优化模型.采用染色体长度可变多目标遗传算法,结合外部惩罚函数对该模型进行了求解,并利用遗传算法中的染色体变长算子对列车操纵手柄变换策略进行了优化.在四显示固定闭塞平台上的仿真结果表明,该方法可在安全、准点的前提下,使追踪列车的能耗下降4.3％,运行时间误差减小1.7％.

Energy-Saving Optimal Control of Following Trains Based on Genetic Algorithm

In order to study the optimum operating strategy for energy saving of the following train in a following operation,the static speed constraints of the trains and the dynamic speed constraints of the following train were put forward under a four-aspect fixed autoblock system.On this basis,an energy-saving optimal operation model of the following train was created taking the train control notch and the corresponding train position as control variables.With the help of the external punishment function,the model was solved by the changeable chromosome length multi-objective genetic algorithm(GA).The shifting strategy of the train control notch was optimized using the chromosome length mutation operator of GA to determine the change times of the train control notch during the whole trip.The simulation result from a four-aspect fixed autoblock system simulation platform shows that the method can reduce the energy consumption and trip time error of the following train by 4.3% and 1.7%,respectively,on the premise of safety and punctuality.