Abstract: With the rapid growth of urban rail transit mileage, the energy-saving optimised control technology of urban rail trains is a hot topic in recent years. The optimised operation of energy-saving is of great economic significance and social benefit to the actual operation of urban rail transit. At present, the research on this issue mainly relies on field trials and experts experience. In this paper, a nonlinearly constrained optimisation scheme based on the combination of cruising speed and cruising distance is proposed for an actual urban rail transit line. The particle swarm optimisation and genetic algorithm in the intelligent computing field are used to solve the energy-saving optimisation operation curve. The experimental results show that the results obtained by particle swarm optimisation and genetic algorithm can guide train operation.

Key words: urban rail transit trains, optimized operation, particle swarm optimization, genetic algorithm