Multi-objective optimization of high-speed train running speed trajectory based on particle swarm and NSGA-II fusion algorithm

In this paper, the optimization method of single-segment velocity trajectory of high-speed train is studied. The optimization process is divided into two stages. Firstly, the energy consumption and travel time are used as the optimization criteria; then the section is divided into several subsections according to different speed constraints, and the optimal speed trajectory search strategy under different track characteristics is proposed. Secondly, talking the speed sequence corresponding to the transition point of the train running state in different sub-intervals as the decision variable, the model in this paper adopts a hybrid algorithm based on multi-objective particle swarm combined with NSGA-II (Non-dominated Sorting Genetic Algorithm-II) in order to obtain the Pareto frontier of the train velocity trajectory with the same satisfaction for all targets.