Multi-Objective Shark Smell Optimization Algorithm Using Incorporated Composite Angle Cosine for Automatic Train Operation
In this paper, an improved multi-objective shark smell optimization algorithm using composite angle cosine is proposed for automatic train operation (ATO). Specifically, when solving the problem that the automatic train operation velocity trajectory optimization easily falls into local optimum, the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/3/714 |
| _version_ | 1797999336512028672 |
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| author | Longda Wang Xingcheng Wang Zhao Sheng Senkui Lu |
| author_facet | Longda Wang Xingcheng Wang Zhao Sheng Senkui Lu |
| author_sort | Longda Wang |
| collection | DOAJ |
| description | In this paper, an improved multi-objective shark smell optimization algorithm using composite angle cosine is proposed for automatic train operation (ATO). Specifically, when solving the problem that the automatic train operation velocity trajectory optimization easily falls into local optimum, the shark smell optimization algorithm with strong searching ability is adopted, and composite angle cosine is incorporated. In addition, the dual-population evolution mechanism is adopted to restrain the aggregation phenomenon in shark population at the end of the iteration to suppress the local convergence. Correspondingly, the composite angle cosine, considering the numerical difference and preference difference, is used as the evaluation index, which ameliorates the shortcoming that the traditional evaluation index is not objective and reasonable. Finally, the Matlab/simulation and hardware-in-the-loop simulation (HILS) results for automatic train operation show that the improved optimization algorithm proposed in this paper has better optimization performance. |
| first_indexed | 2024-04-11T11:03:08Z |
| format | Article |
| id | doaj.art-94304a21dca34b149231573e3769544c |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T11:03:08Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-94304a21dca34b149231573e3769544c2022-12-22T04:28:29ZengMDPI AGEnergies1996-10732020-02-0113371410.3390/en13030714en13030714Multi-Objective Shark Smell Optimization Algorithm Using Incorporated Composite Angle Cosine for Automatic Train OperationLongda Wang0Xingcheng Wang1Zhao Sheng2Senkui Lu3School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, ChinaSchool of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, ChinaSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, ChinaIn this paper, an improved multi-objective shark smell optimization algorithm using composite angle cosine is proposed for automatic train operation (ATO). Specifically, when solving the problem that the automatic train operation velocity trajectory optimization easily falls into local optimum, the shark smell optimization algorithm with strong searching ability is adopted, and composite angle cosine is incorporated. In addition, the dual-population evolution mechanism is adopted to restrain the aggregation phenomenon in shark population at the end of the iteration to suppress the local convergence. Correspondingly, the composite angle cosine, considering the numerical difference and preference difference, is used as the evaluation index, which ameliorates the shortcoming that the traditional evaluation index is not objective and reasonable. Finally, the Matlab/simulation and hardware-in-the-loop simulation (HILS) results for automatic train operation show that the improved optimization algorithm proposed in this paper has better optimization performance.https://www.mdpi.com/1996-1073/13/3/714automatic train operationmulti-objective optimizationshark smell optimization algorithmcomposite angle cosinedual-population evolution mechanismhardware-in-the-loop simulation |
| spellingShingle | Longda Wang Xingcheng Wang Zhao Sheng Senkui Lu Multi-Objective Shark Smell Optimization Algorithm Using Incorporated Composite Angle Cosine for Automatic Train Operation Energies automatic train operation multi-objective optimization shark smell optimization algorithm composite angle cosine dual-population evolution mechanism hardware-in-the-loop simulation |
| title | Multi-Objective Shark Smell Optimization Algorithm Using Incorporated Composite Angle Cosine for Automatic Train Operation |
| title_full | Multi-Objective Shark Smell Optimization Algorithm Using Incorporated Composite Angle Cosine for Automatic Train Operation |
| title_fullStr | Multi-Objective Shark Smell Optimization Algorithm Using Incorporated Composite Angle Cosine for Automatic Train Operation |
| title_full_unstemmed | Multi-Objective Shark Smell Optimization Algorithm Using Incorporated Composite Angle Cosine for Automatic Train Operation |
| title_short | Multi-Objective Shark Smell Optimization Algorithm Using Incorporated Composite Angle Cosine for Automatic Train Operation |
| title_sort | multi objective shark smell optimization algorithm using incorporated composite angle cosine for automatic train operation |
| topic | automatic train operation multi-objective optimization shark smell optimization algorithm composite angle cosine dual-population evolution mechanism hardware-in-the-loop simulation |
| url | https://www.mdpi.com/1996-1073/13/3/714 |
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