Fusion Swarm-Intelligence-Based Decision Optimization for Energy-Efficient Train-Stopping Schemes
To solve the decision problem of train stopping schemes, this paper introduces the static game into the optimal configuration of stopping time to realize the rational decision of train operation. First, a train energy consumption model is constructed with the lowest energy consumption of train opera...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/3/1497 |
| _version_ | 1827760422978060288 |
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| author | Xianguang Jia Xinbo Zhou Jing Bao Guangyi Zhai Rong Yan |
| author_facet | Xianguang Jia Xinbo Zhou Jing Bao Guangyi Zhai Rong Yan |
| author_sort | Xianguang Jia |
| collection | DOAJ |
| description | To solve the decision problem of train stopping schemes, this paper introduces the static game into the optimal configuration of stopping time to realize the rational decision of train operation. First, a train energy consumption model is constructed with the lowest energy consumption of train operation as the optimization objective. In addition, a Mustang optimization algorithm based on cubic chaos mapping, the population hierarchy mechanism, the golden sine strategy, and the Levy flight strategy was designed for solving the problem of it being easy for the traditional population intelligence algorithm to fall into a local optimum when solving complex problems. Lastly, simulation experiments were conducted to compare the designed algorithm with PSO, GA, WOA, GWO, and other cutting-edge optimization algorithms in cross-sectional simulations, and the results show that the algorithm had excellent global optimization finding and convergence capabilities. The simulation results show that the research in this paper can provide effective decisions for the dwell time of trains at multiple stations, and promote the intelligent operation of the train system. |
| first_indexed | 2024-03-11T09:53:00Z |
| format | Article |
| id | doaj.art-6fac283bf97d4a48842e63e09ec355ba |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T09:53:00Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-6fac283bf97d4a48842e63e09ec355ba2023-11-16T16:06:20ZengMDPI AGApplied Sciences2076-34172023-01-01133149710.3390/app13031497Fusion Swarm-Intelligence-Based Decision Optimization for Energy-Efficient Train-Stopping SchemesXianguang Jia0Xinbo Zhou1Jing Bao2Guangyi Zhai3Rong Yan4Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, ChinaTo solve the decision problem of train stopping schemes, this paper introduces the static game into the optimal configuration of stopping time to realize the rational decision of train operation. First, a train energy consumption model is constructed with the lowest energy consumption of train operation as the optimization objective. In addition, a Mustang optimization algorithm based on cubic chaos mapping, the population hierarchy mechanism, the golden sine strategy, and the Levy flight strategy was designed for solving the problem of it being easy for the traditional population intelligence algorithm to fall into a local optimum when solving complex problems. Lastly, simulation experiments were conducted to compare the designed algorithm with PSO, GA, WOA, GWO, and other cutting-edge optimization algorithms in cross-sectional simulations, and the results show that the algorithm had excellent global optimization finding and convergence capabilities. The simulation results show that the research in this paper can provide effective decisions for the dwell time of trains at multiple stations, and promote the intelligent operation of the train system.https://www.mdpi.com/2076-3417/13/3/1497golden-sine strategyMustang optimization algorithmpopulation hierarchy mechanismstatic gamestopping time optimization |
| spellingShingle | Xianguang Jia Xinbo Zhou Jing Bao Guangyi Zhai Rong Yan Fusion Swarm-Intelligence-Based Decision Optimization for Energy-Efficient Train-Stopping Schemes Applied Sciences golden-sine strategy Mustang optimization algorithm population hierarchy mechanism static game stopping time optimization |
| title | Fusion Swarm-Intelligence-Based Decision Optimization for Energy-Efficient Train-Stopping Schemes |
| title_full | Fusion Swarm-Intelligence-Based Decision Optimization for Energy-Efficient Train-Stopping Schemes |
| title_fullStr | Fusion Swarm-Intelligence-Based Decision Optimization for Energy-Efficient Train-Stopping Schemes |
| title_full_unstemmed | Fusion Swarm-Intelligence-Based Decision Optimization for Energy-Efficient Train-Stopping Schemes |
| title_short | Fusion Swarm-Intelligence-Based Decision Optimization for Energy-Efficient Train-Stopping Schemes |
| title_sort | fusion swarm intelligence based decision optimization for energy efficient train stopping schemes |
| topic | golden-sine strategy Mustang optimization algorithm population hierarchy mechanism static game stopping time optimization |
| url | https://www.mdpi.com/2076-3417/13/3/1497 |
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