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Research on Optimization of Freight Train ATO Based on Elite Competition Multi-Objective Particle Swarm Optimization
Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, College of Automation and Electronics Information, Xiangtan University, Xiangtan, China
Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, College of Automation and Electronics Information, Xiangtan University, Xiangtan, China
The State Key Laboratory of Heavy Duty AC Drive Electric Locomotive Systems Integration, Zhuzhou, China
Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, College of Automation and Electronics Information, Xiangtan University, Xiangtan, China
Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, College of Automation and Electronics Information, Xiangtan University, Xiangtan, China
Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, College of Automation and Electronics Information, Xiangtan University, Xiangtan, China
- 1 Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, College of Automation and Electronics Information, Xiangtan University, Xiangtan, China
- 2 Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, College of Automation and Electronics Information, Xiangtan University, Xiangtan, China
- 3 The State Key Laboratory of Heavy Duty AC Drive Electric Locomotive Systems Integration, Zhuzhou, China
- 4 Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, College of Automation and Electronics Information, Xiangtan University, Xiangtan, China
- 5 Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, College of Automation and Electronics Information, Xiangtan University, Xiangtan, China
- 6 Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, College of Automation and Electronics Information, Xiangtan University, Xiangtan, China
Energy and Power Engineering·Volume 13 (2021)·Pages 41–51·Published 8 April 2021·DOI10.4236/epe.2021.134B005
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Abstract
In view of the complex problems that freight train ATO (automatic train operation) needs to comprehensively consider punctuality, energy saving and safety, a dynamics model of the freight train operation process is established based on the safety and the freight train dynamics model in the process of its operation. The algorithm of combining elite competition strategy with multi-objective particle swarm optimization technology is introduced, and the winning particles are obtained through the competition between two elite particles to guide the update of other particles, so as to balance the convergence and distribution of multi-objective particle swarm optimization. The performance comparison experimental results verify the superiority of the proposed algorithm. The simulation experiments of the actual line verify the feasibility of the model and the effectiveness of the proposed algorithm.
KeywordsFreight Train Automatic Train OperationDynamics ModelCompetitive Multi-Objective Particle Swarm Optimization Algorithm (CMOPSO)Multi-Objective Optimization
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