Research on Multi-Objective Optimization of ATO Based on Adaptive Learning Mixed-Strategy Particle Swarm Algorithm

For the multi-objective optimization problem of energy saving, comfort, punctuality and on-time performance in the process of Automatic Train Operation (ATO) of high-speed trains, a solution algorithm based on particle swarm algorithm with adaptive hybrid strategy is proposed. Firstly, for the inaccuracy of the force analysis of single-mass point modeling of high-speed train, a rigid multi-mass point model of high-speed train is established; secondly, using the dynamics model of high-speed trains and the safety of train operation as constraints, the affiliation function is used to establish a multi-objective optimization model of high-speed train ATO, when dealing with the constraints, the high-speed train stopping error and the line speed limit are used as penalty items to construct a suitable penalty function to be added to the objective function, which constitutes the fitness function used in this paper; finally, in order to solve the shortcomings of the particle swarm optimization algorithm that is easy to converge and easy to fall into the local optimum, the adaptive learning mixed strategy particle swarm optimization algorithm is proposed. Experimental validation is carried out by selecting real routes and high-speed trains to verify the effectiveness of the method proposed in the paper in reducing the energy consumption of high-speed train operation, improving comfort, and arriving at the destination on time and on schedule.