An Improved Whale Optimization Algorithm via Angle Penalized Distance for Automatic Train Operation
This study proposes a novel effective improved whale optimization algorithm via angle penalized distance (IWOA-APD) for automatic train operation (ATO) to effectively improve the ATO quality. Specifically, aiming at the high-quality target speed curve of urban rail trains, a target speed curve multi...
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MDPI AG
2025-06-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/10/6/384 |
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| author | Longda Wang Yanjie Ju Long Guo Gang Liu Chunlin Li Yan Chen |
| author_facet | Longda Wang Yanjie Ju Long Guo Gang Liu Chunlin Li Yan Chen |
| author_sort | Longda Wang |
| collection | DOAJ |
| description | This study proposes a novel effective improved whale optimization algorithm via angle penalized distance (IWOA-APD) for automatic train operation (ATO) to effectively improve the ATO quality. Specifically, aiming at the high-quality target speed curve of urban rail trains, a target speed curve multi-objective optimization model for ATO is established with energy saving, punctuality, accurate stopping, and comfort as the indexes; and the comprehensive evaluation strategy utilizing angle-penalized distance as the evaluation index is proposed to enhance the assessment’s rationality and applicability. On this basis, the IWOA-APD is proposed using strategies of non-linear decreasing convergence factor, solutions of out-of-bounds eliminating via combination of reflection and refraction, mechanisms of genetic evolution with variable probability, and elite maintenance based on fusion distance and crowding degree distance. In addition, the detailed design scheme of IWOA-APD is given. The test results show that the proposed IWOA-APD achieves significant performance improvements compared to traditional MOWOA. In the optimization scenario from Lvshun New Port Station to Tieshan Town Station of Dalian urban rail transit line No.12, the IGD value shows a remarkable 69.1% reduction, while energy consumption decreases by 12.5%. The system achieves a 64.6% improvement in punctuality and a 76.5% enhancement in parking accuracy. Additionally, comfort level improves by 15.9%. |
| format | Article |
| id | doaj-art-68c4d3e7b26543bb94f89f2e551f47d7 |
| institution | Kabale University |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-68c4d3e7b26543bb94f89f2e551f47d72025-08-20T03:27:25ZengMDPI AGBiomimetics2313-76732025-06-0110638410.3390/biomimetics10060384An Improved Whale Optimization Algorithm via Angle Penalized Distance for Automatic Train OperationLongda Wang0Yanjie Ju1Long Guo2Gang Liu3Chunlin Li4Yan Chen5School of Electrical Engineering, Dalian Jiaotong University, Dalian 116023, ChinaSchool of Electrical Engineering, Dalian Jiaotong University, Dalian 116023, ChinaSchool of Economics and Management, Gongqing Institute of Science and Technology, Jiujiang 332020, ChinaCollege of Engineering, Inner Mongolia Minzu University, Tongliao 028000, ChinaFaculty of Vehicle Engineering and Mechanics, Dalian University of Technology, Dalian 116023, ChinaSchool of Mechanical and Electrical Engineering, Chizhou University, Chizhou 247000, ChinaThis study proposes a novel effective improved whale optimization algorithm via angle penalized distance (IWOA-APD) for automatic train operation (ATO) to effectively improve the ATO quality. Specifically, aiming at the high-quality target speed curve of urban rail trains, a target speed curve multi-objective optimization model for ATO is established with energy saving, punctuality, accurate stopping, and comfort as the indexes; and the comprehensive evaluation strategy utilizing angle-penalized distance as the evaluation index is proposed to enhance the assessment’s rationality and applicability. On this basis, the IWOA-APD is proposed using strategies of non-linear decreasing convergence factor, solutions of out-of-bounds eliminating via combination of reflection and refraction, mechanisms of genetic evolution with variable probability, and elite maintenance based on fusion distance and crowding degree distance. In addition, the detailed design scheme of IWOA-APD is given. The test results show that the proposed IWOA-APD achieves significant performance improvements compared to traditional MOWOA. In the optimization scenario from Lvshun New Port Station to Tieshan Town Station of Dalian urban rail transit line No.12, the IGD value shows a remarkable 69.1% reduction, while energy consumption decreases by 12.5%. The system achieves a 64.6% improvement in punctuality and a 76.5% enhancement in parking accuracy. Additionally, comfort level improves by 15.9%.https://www.mdpi.com/2313-7673/10/6/384automatic train operationtarget speed curvemulti-objective optimizationimproved whale optimization algorithm |
| spellingShingle | Longda Wang Yanjie Ju Long Guo Gang Liu Chunlin Li Yan Chen An Improved Whale Optimization Algorithm via Angle Penalized Distance for Automatic Train Operation Biomimetics automatic train operation target speed curve multi-objective optimization improved whale optimization algorithm |
| title | An Improved Whale Optimization Algorithm via Angle Penalized Distance for Automatic Train Operation |
| title_full | An Improved Whale Optimization Algorithm via Angle Penalized Distance for Automatic Train Operation |
| title_fullStr | An Improved Whale Optimization Algorithm via Angle Penalized Distance for Automatic Train Operation |
| title_full_unstemmed | An Improved Whale Optimization Algorithm via Angle Penalized Distance for Automatic Train Operation |
| title_short | An Improved Whale Optimization Algorithm via Angle Penalized Distance for Automatic Train Operation |
| title_sort | improved whale optimization algorithm via angle penalized distance for automatic train operation |
| topic | automatic train operation target speed curve multi-objective optimization improved whale optimization algorithm |
| url | https://www.mdpi.com/2313-7673/10/6/384 |
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