基于遗传算法和粒子群优化的列车自动驾驶速度曲线优化方法

针对列车自动驾驶(ATO)过程中的精准停车、准时性、舒适性以及能耗问题,提出一种基于遗传算法与粒子群优化(GAPSO)算法结合的ATO速度曲线优化方法.首先,建立列车ATO运行多目标优化模型,将列车过分相区断电惰行纳入控制策略,并对运行控制策略进行分析;其次,对粒子群优化(PSO)算法进行改进,采用非线性动态惯性权重和...

Optimization method of automatic train operation speed curve based on genetic algorithm and particle swarm optimization

Aiming at the problems of precise parking， punctuality， comfort and energy consumption in the process of Automatic Train Operation （ATO）， an optimization method of ATO speed curve based on GAPSO （Genetic Algorithm and Particle Swarm Optimization） algorithm was proposed. Firstly， a multi-objective optimization model of train ATO operation was established， the train passing through the neutral zone with power cutoff and coasting was included in the control strategy， and the operation control strategy was analyzed. Secondly， Particle Swarm Optimization （PSO） algorithm was improved， the nonlinear dynamic inertia weight and the improved acceleration coefficient were adopted， and the genetic operator was integrated into it to form a brand-new GAPSO algorithm， and the superiority of GAPSO algorithm in global search and local search ability as well as convergence speed was verified. Finally， GAPSO algorithm was used to optimize the operating mode changing points， and a set of operating mode changing point speeds satisfying multi-objective optimization was obtained， thereby obtaining the optimal target speed curve. Simulation experimental results show that under the premise that the overall running time meets the requirements of punctuality， the optimization method can make the energy consumption reduced by 13.29%， the comfort increased by 26.62%， and the parking error reduced by 21.62%. Therefore， the optimized train target speed curve can meet the multi-objective requirements， and this method provides a feasible solution for train ATO multi-objective optimization.