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

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.