Open Access

ARTICLE

Operation Optimal Control of Urban Rail Train Based on Multi-Objective Particle Swarm Optimization

by Liang Jin^1,*, Qinghui Meng¹, Shuang Liang²

1 Department of Mechanical and Electrical, Henan Polytechnic Institute, Nanyang Henan, 473000, China
2 University of Florence, Firenze, 50041, Italy

* Corresponding Author: Liang Jin. Email:

Computer Systems Science and Engineering 2022, 42(1), 387-395. https://doi.org/10.32604/csse.2022.017745

Received 09 February 2021; Accepted 04 May 2021; Issue published 02 December 2021

Abstract

The energy consumption of train operation occupies a large proportion of the total consumption of railway transportation. In order to improve the operating energy utilization rate of trains, a multi-objective particle swarm optimization (MPSO) algorithm with energy consumption, punctuality and parking accuracy as the objective and safety as the constraint is built. To accelerate its the convergence process, the train operation progression is divided into several modes according to the train speed-distance curve. A human-computer interactive particle swarm optimization algorithm is proposed, which presents the optimized results after a certain number of iterations to the decision maker, and the satisfactory outcomes can be obtained after a limited number of adjustments. The multi-objective particle swarm optimization (MPSO) algorithm is used to optimize the train operation process. An algorithm based on the important relationship between the objective and the preference information of the given reference points is suggested to overcome the shortcomings of the existing algorithms. These methods significantly increase the computational complexity and convergence of the algorithm. An adaptive fuzzy logic system that can simultaneously utilize experience information and field data information is proposed to adjust the consequences of off-line optimization in real time, thereby eliminating the influence of uncertainty on train operation. After optimization and adjustment, the whole running time has been increased by 0.5 s, the energy consumption has been reduced by 12%, the parking accuracy has been increased by 8%, and the comprehensive performance has been enhanced.

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Keywords

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