Multi-Objective Optimization of Speed Profile for Railway Catenary Maintenance Vehicle Operations Based on Improved Non-Dominated Sorting Genetic Algorithm III - Bingli Zhang; Gan Shen; Yixin Wang; Yangyang Zhang; Chengbiao Zhang; Xinyu Wang; Zhongzheng Liu; Xiang Luo (Applied Sciences)
Railway catenary maintenance vehicles are essential for ensuring the safety and efficiency of electrified railway systems. The implementation of pre-optimized speed profiles significantly reduces the energy consumption while improving key operational performance metrics, such as ride comfort, punctuality, and safety. This study introduces a novel multi-objective optimization method that optimizes the speed profile in scenarios in which railway catenary maintenance vehicles are performing operations on line sections. Initially, a multi-objective optimization model is developed based on a four-stage operational strategy. Subsequently, the enhanced selection strategy of the Non-Dominated Sorting Genetic Algorithm III (ESS-NSGA-III) algorithm is proposed to refine the mating and environmental selection processes. Finally, the effectiveness of the proposed method is validated using the Huoqiu-Caomiao section of the Fuyang-Lu’an Railway in China. A comparative analysis demonstrates that the ESS-NSGA-III algorithm outperforms NSGA-III and NSGA-II in terms of the diversity and convergence of the solution set. Specifically, the Hypervolume (HV) index improves by 0.77% and 4.12% compared to NSGA-III and NSGA-II, respectively. Moreover, the results highlight the advantages of the proposed method based on a comparison of three alternative operational strategies. Compared to the minimum running time strategy, the punctual and delayed strategies achieve energy consumption reductions of 29.51% and 52.86%, respectively. These results validate the algorithm’s capability to provide valuable insights for practical applications.