Research on Optimization of Freight Train ATO Based on Elite Competition Multi-Objective Particle Swarm Optimization  

作　　者：Lingzhi Yi Renzhe Duan Wang Li Yihao Wang Dake Zhang Bo Liu Lingzhi Yi;Renzhe Duan;Wang Li;Yihao Wang;Dake Zhang;Bo Liu(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)

机构地区：[1]Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, College of Automation and Electronics Information, Xiangtan University, Xiangtan, China [2]The State Key Laboratory of Heavy Duty AC Drive Electric Locomotive Systems Integration, Zhuzhou, China

出　　处：《Energy and Power Engineering》2021年第4期41-51,共11页能源与动力工程（英文）

摘　　要：<div style="text-align:justify;"> 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. </div><div style="text-align:justify;"> 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. </div>

关 键 词：Freight Train Automatic Train Operation Dynamics Model Competitive Multi-Objective Particle Swarm Optimization Algorithm (CMOPSO) Multi-Objective Optimization 

分 类 号：F42[经济管理—产业经济]