Open Access

ARTICLE

Study on Optimization of Urban Rail Train Operation Control Curve Based on Improved Multi-Objective Genetic Algorithm

by Xiaokan Wang, Qiong Wang

Henan Mechanical and Electrical Vocational College, Xinzheng, 451191, China

* Corresponding Author: Xiaokan Wang. Email:

Journal on Internet of Things 2021, 3(1), 1-9. https://doi.org/10.32604/jiot.2021.010228

Received 12 October 2020; Accepted 07 December 2020; Issue published 16 March 2021

Abstract

A multi-objective improved genetic algorithm is constructed to solve the train operation simulation model of urban rail train and find the optimal operation curve. In the train control system, the conversion point of operating mode is the basic of gene encoding and the chromosome composed of multiple genes represents a control scheme, and the initial population can be formed by the way. The fitness function can be designed by the design requirements of the train control stop error, time error and energy consumption. the effectiveness of new individual can be ensured by checking the validity of the original individual when its in the process of selection, crossover and mutation, and the optimal algorithm will be joined all the operators to make the new group not eliminate on the best individual of the last generation. The simulation result shows that the proposed genetic algorithm comparing with the optimized multi-particle simulation model can reduce more than 10% energy consumption, it can provide a large amount of sub-optimal solution and has obvious optimization effect.

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Keywords

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