Optimization of train energy saving based on golden ratio genetic algorithm

Abstract

In order to reduce the energy consumption of train operation, an optimization method based on genetic algorithm of golden section is proposed. Firstly, the Multi-particle train model is established. Secondly, the optimal operation strategy of subway trains is analyzed according to different ramps. Then, a golden section genetic algorithm (GR-GA) is proposed to solve the problem that genetic algorithm is easy to fall into local optimum. A golden section genetic algorithm (GR-GA) is proposed to search for the optimal transfer position of train and the best adaptive point of searching crossover and mutation operator with golden ratio is introduced, which improves the local optimization ability and convergence performance. Taking Yizhuang line as a simulation case, the results show that the proposed algorithm has a better optimization effect.