Open Access
ARTICLE
Study on Optimization of Urban Rail Train Operation Control Curve Based on Improved Multi-Objective Genetic Algorithm
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.
Keywords
Cite This Article
X. Wang and Q. Wang, "Study on optimization of urban rail train operation control curve based on improved multi-objective genetic algorithm,"
Journal on Internet of Things, vol. 3, no.1, pp. 1–9, 2021.