Optimization of High-Speed Train Control Strategy for Traction Energy Saving Using an Improved Genetic Algorithm
Joint Authors
Wen, Tao
Su, Ruidan
Gu, Qianrong
Source
Journal of Applied Mathematics
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-04-30
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
A parallel multipopulation genetic algorithm (PMPGA) is proposed to optimize the train control strategy, which reduces the energy consumption at a specified running time.
The paper considered not only energy consumption, but also running time, security, and riding comfort.
Also an actual railway line (Beijing-Shanghai High-Speed Railway) parameter including the slop, tunnel, and curve was applied for simulation.
Train traction property and braking property was explored detailed to ensure the accuracy of running.
The PMPGA was also compared with the standard genetic algorithm (SGA); the influence of the fitness function representation on the search results was also explored.
By running a series of simulations, energy savings were found, both qualitatively and quantitatively, which were affected by applying cursing and coasting running status.
The paper compared the PMPGA with the multiobjective fuzzy optimization algorithm and differential evolution based algorithm and showed that PMPGA has achieved better result.
The method can be widely applied to related high-speed train.
American Psychological Association (APA)
Su, Ruidan& Gu, Qianrong& Wen, Tao. 2014. Optimization of High-Speed Train Control Strategy for Traction Energy Saving Using an Improved Genetic Algorithm. Journal of Applied Mathematics،Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-477134
Modern Language Association (MLA)
Su, Ruidan…[et al.]. Optimization of High-Speed Train Control Strategy for Traction Energy Saving Using an Improved Genetic Algorithm. Journal of Applied Mathematics No. 2014 (2014), pp.1-7.
https://search.emarefa.net/detail/BIM-477134
American Medical Association (AMA)
Su, Ruidan& Gu, Qianrong& Wen, Tao. Optimization of High-Speed Train Control Strategy for Traction Energy Saving Using an Improved Genetic Algorithm. Journal of Applied Mathematics. 2014. Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-477134
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-477134