Research on train energy optimization based on the improved atomic orbital search algorithm

This paper presents an energy-saving optimization strategy for train operations. A mathematical optimization model was developed under various constraints, including safety, timeliness, precision stopping, and comfort levels. An improved hybrid algorithm, AOS-PSO, was proposed, combining the local search capability of the Atomic Orbital Search (AOS) algorithm with the global exploration capacity of Particle Swarm Optimization (PSO). Simulation experiments were conducted using real train and route data, and the results demonstrate that the AOS-PSO algorithm significantly improves the energy efficiency of train operations. Compared to pre-optimization, the proposed method reduced energy consumption, enhanced comfort, and ensured precise train stops while maintaining punctuality.