Research on Multi-objective Optimal Control of Train Operation Based on EOL-NSGA-III Algorithm

Wenxiang Liu, Xiao Juan Liu, Chenge Song, Hong Zhi Lv
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Abstract

In view of the functional demands of the automatic train operation (ATO) system of urban rail transit, under the conditions of train operation safety, speed limit and train dynamics performance constraints, the multi-objective optimization model for the train operation is built with low energy consumption, stopping accuracy, punctuality and passenger comfort as control objectives. Under the MATLAB environment, first comparing the Pareto optimal solution of NSGA-III algorithm with Non-dominated Sorting Genetic Algorithm III based on Elite opposition-based Learn (EOL- NSGA-III) algorithm, then based on the Beijing Yizhuang Line interval route data, the EOL-NSGA-III algorithm is applied to solve the multi-objective optimization model. The simulation results confirm the feasibility of the EOL-NSGA-III algorithm and the effectiveness of the multi-objective optimization model, thereby designing an efficient multi-objective operation of urban rail transit trains control strategy.
基于EOL-NSGA-III算法的列车运行多目标最优控制研究
针对城市轨道交通列车自动运行(ATO)系统的功能需求,在列车运行安全、限速和列车动力学性能约束条件下,以低能耗、停车精度、正点率和乘客舒适度为控制目标,建立了列车运行的多目标优化模型。在MATLAB环境下,首先将NSGA-III算法的Pareto最优解与基于精英对立学习的非支配排序遗传算法III (EOL- NSGA-III)算法进行比较,然后基于北京义庄线区间路线数据,应用EOL-NSGA-III算法求解多目标优化模型。仿真结果验证了EOL-NSGA-III算法的可行性和多目标优化模型的有效性,从而设计出一种高效的城市轨道交通列车多目标运行控制策略。
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