FA-Based Optimal Strategy Study of Train's Energy Saving Operation under Disturbed Condition

Hui Hu

doi:10.4028/www.scientific.net/AMM.339.50

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 339FA-Based Optimal Strategy Study of Train's Energy...

FA-Based Optimal Strategy Study of Train's Energy Saving Operation under Disturbed Condition

Abstract:

Different from changeable chromosome length genetic algorithm, a new method based on firefly optimization algorithm is put forward to search a trains energy saving operation strategy under running disturbance condition in railway network. The algorithm is designed and validated using a referenced simulation case. Compared with other methods, it demonstrates this new FA-based algorithm has a better result in computation efficiency and may be considered to use in other energy efficient train operation models.

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Periodical:

Applied Mechanics and Materials (Volume 339)

Pages:

50-54

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.339.50

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Online since:

July 2013

Authors:

Hui Hu

Keywords:

Energy Saving, Firefly Algorithm, Operation Strategy, Train Running Disturbance

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References

[1] China Transportation Annual. 2006 China Transportation Annual[M]. Beijing: China Transportation Annual Publishing House,2006.

Google Scholar

[2] Howlett P G. Optimal Strategies for the Control of a Train. Automatica, 1996,32(4):519-532.

DOI: 10.1016/0005-1098(95)00184-0

Google Scholar

[3] Cheng Jiaxing. Optimal Algorithm for Energy Saving of Train Operation [J]. Micro-Computer Development,1999,15(2):1-4.

Google Scholar

[4] Shi Hongguo. Study on Train Operation and Movement Process Simulation and its Optimization[D]. Southwest Jiaotong University, 2006.9.

Google Scholar

[5] Fu Yinping, Gao Ziyou, Li Keping. Optimization Method of Energy Saving Train Operation for Railway Network. Journal of Transportation Systems Engineering and Information Technology, 2009,9(4): 90-96.

DOI: 10.1016/s1570-6672(08)60074-4

Google Scholar

[6] X.S. Yang, Firefly algorithms for multimodal optimization, Proceeding of the Stochastic Algorithms Foundations and Applications (SAGA'09),vol.5792 of Lecture Notes in Computing Sciences. Springer,Sapporo, Japan, October 2009:178-178.

DOI: 10.1007/978-3-642-04944-6_14

Google Scholar

[7] X.S. Yang, Firefly algorithm, Levy flights and global optimization, in Research and Development in Intelligent Systems XXVI. Springer, London, UK,2010:209-218.

DOI: 10.1007/978-1-84882-983-1_15

Google Scholar

[8] X.S. Yang, Firefly algorithm, stochastic test functions and design optimisation, International Journal of Bio-Inspired Comutation, 2010,2(2): 78-84.

DOI: 10.1504/ijbic.2010.032124

Google Scholar