Research on Train Energy-Saving Optimization Based on Parallel Immune Particle Swarm Optimization

Aiming at the optimization problem of subway train energy conservation, this paper proposes a train energy-saving optimization method based on parallel immune particle swarm optimization. The parallel immune particle swarm optimization algorithm is used to optimize the train energy saving in two stages: firstly, the running time of each interval is fixed, the algorithm is used to search for the optimal working condition switching point, and then, the train running time is optimized under the premise of constant running time. Finally, using the real data of Beijing Yizhuang line to simulate, verify the feasibility of the model and algorithm.