Optimization of speed curve for heavy-haul trains based on Multi-strategy Hybrid Sparrow Search Algorithm

To address the optimization of heavy-haul train speed curves, a novel optimization method based on a multistrategy hybrid sparrow search algorithm (MHSSA) is proposed. First, a multi-objective optimization model is developed, targeting both train punctuality and energy efficiency. The traditional sparrow search algorithm is then enhanced, and the superiority of the improved algorithm in global search capabilities and convergence speed is validated through benchmark test functions. Finally, an energy-saving train operation strategy is designed and verified using data from a heavy-haul railway line in China. Simulation results demonstrate that the energy consumption of two strategies optimized by the MHSSA algorithm is reduced by $21.55 \%$ and $35.08 \%$, respectively, while ensuring punctuality. Comparative analysis of different train operation strategies reveals that incorporating coasting conditions into the operation significantly enhances energy savings.