• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 26 Issue 5
Oct.  2013
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Article Contents
LI Wei, PU Hao, ZHAO Haifeng, HU Jianping, MENG Cunxi. Intelligent Railway Aignment Optimization Based on Stepwise Encoding Genetic Algorithm[J]. Journal of Southwest Jiaotong University, 2013, 26(5): 831-838. doi: 10.3969/j.issn.0258-2724.2013.05.008
Citation: LI Wei, PU Hao, ZHAO Haifeng, HU Jianping, MENG Cunxi. Intelligent Railway Aignment Optimization Based on Stepwise Encoding Genetic Algorithm[J]. Journal of Southwest Jiaotong University, 2013, 26(5): 831-838. doi: 10.3969/j.issn.0258-2724.2013.05.008

Intelligent Railway Aignment Optimization Based on Stepwise Encoding Genetic Algorithm

doi: 10.3969/j.issn.0258-2724.2013.05.008
  • Received Date: 26 Sep 2012
  • Publish Date: 25 Oct 2013
  • In order to achieve the optimization of railway 3D alignments, an optimization model was built, in which comprehensive factors including construction, operation, environment, and constraints were embedded. Different distributions for horizontal and vertical control points were presented. A genetic series that consists of offsets of intersection points, the radii of the circular curve, and the elevations of grade change points was designed. Then a stepwise horizontal-vertical-integral genetic encoding method was put forward, and the genetic operators for crossover and mutation were also designed to achieve the optimization of railway alignments. The results of application examples indicate that this method can overcome the shortcoming of the overlapping of horizontal curves and vertical curves, and yield a group of alignments with low comprehensive costs and meanwhile conforming to railway constraints. The optimal alignment obtained by this optimization method can reduce the overall comprehensive cost by 6.5% than the alignment obtained by human work.

     

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