Multi-Objective Optimization of Speed Profile for Railway Catenary Maintenance Vehicle Operations Based on Improved Non-Dominated Sorting Genetic Algorithm III
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, punctu...
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MDPI AG
2025-04-01
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| author | Bingli Zhang Gan Shen Yixin Wang Yangyang Zhang Chengbiao Zhang Xinyu Wang Zhongzheng Liu Xiang Luo |
| author_facet | Bingli Zhang Gan Shen Yixin Wang Yangyang Zhang Chengbiao Zhang Xinyu Wang Zhongzheng Liu Xiang Luo |
| author_sort | Bingli Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
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| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-549fc7abfa4a433e8d37ad9fe36515eb2025-08-20T02:17:21ZengMDPI AGApplied Sciences2076-34172025-04-01158436110.3390/app15084361Multi-Objective Optimization of Speed Profile for Railway Catenary Maintenance Vehicle Operations Based on Improved Non-Dominated Sorting Genetic Algorithm IIIBingli Zhang0Gan Shen1Yixin Wang2Yangyang Zhang3Chengbiao Zhang4Xinyu Wang5Zhongzheng Liu6Xiang Luo7School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, ChinaSchool of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, ChinaSchool of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, ChinaSchool of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, ChinaSchool of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, ChinaSchool of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, ChinaSchool of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, ChinaSchool of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, ChinaRailway 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.https://www.mdpi.com/2076-3417/15/8/4361railway catenary maintenance vehiclespeed profilemulti-objective optimizationoperation strategyenhanced selection strategyNSGA-III |
| spellingShingle | Bingli Zhang Gan Shen Yixin Wang Yangyang Zhang Chengbiao Zhang Xinyu Wang Zhongzheng Liu Xiang Luo Multi-Objective Optimization of Speed Profile for Railway Catenary Maintenance Vehicle Operations Based on Improved Non-Dominated Sorting Genetic Algorithm III Applied Sciences railway catenary maintenance vehicle speed profile multi-objective optimization operation strategy enhanced selection strategy NSGA-III |
| title | Multi-Objective Optimization of Speed Profile for Railway Catenary Maintenance Vehicle Operations Based on Improved Non-Dominated Sorting Genetic Algorithm III |
| title_full | Multi-Objective Optimization of Speed Profile for Railway Catenary Maintenance Vehicle Operations Based on Improved Non-Dominated Sorting Genetic Algorithm III |
| title_fullStr | Multi-Objective Optimization of Speed Profile for Railway Catenary Maintenance Vehicle Operations Based on Improved Non-Dominated Sorting Genetic Algorithm III |
| title_full_unstemmed | Multi-Objective Optimization of Speed Profile for Railway Catenary Maintenance Vehicle Operations Based on Improved Non-Dominated Sorting Genetic Algorithm III |
| title_short | Multi-Objective Optimization of Speed Profile for Railway Catenary Maintenance Vehicle Operations Based on Improved Non-Dominated Sorting Genetic Algorithm III |
| title_sort | multi objective optimization of speed profile for railway catenary maintenance vehicle operations based on improved non dominated sorting genetic algorithm iii |
| topic | railway catenary maintenance vehicle speed profile multi-objective optimization operation strategy enhanced selection strategy NSGA-III |
| url | https://www.mdpi.com/2076-3417/15/8/4361 |
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