An Improved Adaptive Particle Swarm Optimization Method for High-speed Train Scheduling in Unexpected Events

In order to reduce total delay time and energy consumption of trains in unexpected events which cause operation delay of trains, this paper investigates an optimization scheduling scheme for high-speed trains. First, for the train scheduling model, the objective function is designed which considers the total delay time and energy consumption. Then multiple operation constraints of trains are proposed. Afterwards, to solve the scheduling problem with multiple constraints, an improved adaptive particle swarm optimization (APSO) algorithm is designed which can optimize train delay time and energy consumption to reduce the effect of unexpected events. With the proposed APSO method, the premature convergence can be avoided and more optimal solution can be searched. Meantime, the searching speed can also be guaranteed through the adaptive adjustment strategy for acceleration coefficients. Finally, simulation results are given to illustrate the effectiveness of the proposed APSO algorithm for the train scheduling problem.