横风下基于滚动GAPSO算法的列车速度曲线优化OA

Aiming at the problem that the target speed curve optimization algorithm of high-speed train is not suitable for the cross-wind environment in ordinary environment,a train speed curve optimization method based on rolling GAPSO(genetic particle swarm optimization)algorithm in cross-wind environment is proposed.Firstly,the train dynamics model is improved by considering the cross-wind wind resistance,and the multi-objective optimization model of train operation is established.Secondly,based on the GAPSO algorithm,the optimal target speed curve of the train in the initial stage is constructed,and the rolling optimization framework is introduced to adjust the target speed curve in real time,and the train runs according to the improved fast speed strategy in the cross-wind speed limit area.Finally,GAPSO algorithm is used to find the optimal idling point to generate the target velocity curve before the train enters the station.The simulation results show that GAPSO algorithm has the advantages of strong searching ability and fast convergence speed compared with GA algorithm and PSO algorithm.The rolling GAPSO algorithm can generate the optimized target velocity curve in real time under different cross-wind environments,and has better energy saving and punctuality than the improved fast travel strategy and RH-PSO algorithm.The optimization of train target speed curve based on rolling GAPSO algorithm under cross-wind can provide a feasible solution for train energy saving and on-time operation under cross-wind environment.