Multi-objective operation optimization study of high-speed train ATO based on improved particle swarm algorithm

Ning Wang; Peng Li; Jianqiang Shi; Zongshou Wei

doi:10.1117/12.2686349

10 August 2023 Multi-objective operation optimization study of high-speed train ATO based on improved particle swarm algorithm

Ning Wang, Peng Li, Jianqiang Shi, Zongshou Wei

Author Affiliations +

Proceedings Volume 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023); 127591U (2023) https://doi.org/10.1117/12.2686349
Event: 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), 2023, Xiamen, China

Abstract

A speed profile optimization method based on improved particle swarm algorithm (IPSO) is proposed to address the problems of on-time performance, comfort and energy consumption in the process of automatic train operation (ATO) of high-speed trains. Since the traditional particle swarm algorithm (PSO) has many shortcomings, this paper adopts the nonlinear inertia weight decrement of cosine instead of the linear inertia weight decrement of the traditional particle swarm algorithm, as well as the adaptive adjustment strategy for the acceleration factor, which expands the global search range and avoids the local optimum, while accelerates the convergence speed. Taking the safety of train operation and the train dynamics model as constraints, a multi-objective operation optimization model for high-speed trains is established. The multi-objective operation optimization model of high-speed trains is solved by combining the improved particle swarm algorithm with the maneuvering conditions. Finally, the effectiveness of the improved particle swarm algorithm is verified through the simulation of the selected lines, which makes each performance index relatively optimal.

Citation Download Citation

Ning Wang, Peng Li, Jianqiang Shi, and Zongshou Wei "Multi-objective operation optimization study of high-speed train ATO based on improved particle swarm algorithm", Proc. SPIE 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023), 127591U (10 August 2023); https://doi.org/10.1117/12.2686349

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