@Article{jiot.2021.010228,
AUTHOR = {Xiaokan Wang, Qiong Wang},
TITLE = {Study on Optimization of Urban Rail Train Operation Control Curve Based on Improved Multi-Objective Genetic Algorithm},
JOURNAL = {Journal on Internet of Things},
VOLUME = {3},
YEAR = {2021},
NUMBER = {1},
PAGES = {1--9},
URL = {http://www.techscience.com/jiot/v3n1/41733},
ISSN = {2579-0080},
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.},
DOI = {10.32604/jiot.2021.010228}
}