基于改进MH算法的高铁ATO运行操纵多目标优化研究

在高速列车运行过程中,运行环境变化将对ATO提出更高的计算要求,ATO既要满足实时计算又要满足运行操纵多目标优化。针对该问题提出一种改进MH算法计算列车运行操纵序列。在原有MH算法基础上,对算法计算频率与寻优目标函数进行改进,提出随机惯性权重粒子群算法与司机驾驶逻辑相结合的方式计算运行操纵序列,通过选取合理的计算间隔时间使得算法性能最佳。以武广高铁85km线路仿真高铁自动驾驶,改进MH算法中随机惯性权重粒子群算法的收敛速度优于原算法。仿真结果表明:改进MH算法在列车运行准点性和停车精度方面得到提升,为ATO在线计算提供思路。

Study on multi-objective optimization of high-speed railway ATO operation process based on improved MH algorithm

During the operation of a high-speed train,the change of operation environment will put forward higher calculation requirements for ATO(Automatic Train Operation system),ATO should meet both real-time calculation and multi-objective optimization of operation process.To solve this problem,an improved MH algorithm(Moving Horizon algorithm)was proposed to calculate the train operation sequence.Based on the original MH algorithm,the calculation frequency and optimization objective function of the algorithm were improved.The combination of stochastic inertia weight particle swarm optimization algorithm and driver's driving logic was proposed to calculate the operation sequence,choosing reasonable calculation interval time made the algorithm performance best.Simulate train’s automatic operation in 85 km high-speed railway of Wuhan-Guangzhou line,in the improved MH algorithm,the convergence speed of stochastic inertia weight particle swarm optimization algorithm is better than those of the original algorithm.The simulation results showed that the improved MH algorithm can achieve better train running punctuality and stopping accuracy effect and provide ideas for ATO real-time calculation.