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基于分步编码改进遗传算法的铁路智能选线

李伟 蒲浩 赵海峰 胡建平 孟存喜

李伟, 蒲浩, 赵海峰, 胡建平, 孟存喜. 基于分步编码改进遗传算法的铁路智能选线[J]. 西南交通大学学报, 2013, 26(5): 831-838. doi: 10.3969/j.issn.0258-2724.2013.05.008
引用本文: 李伟, 蒲浩, 赵海峰, 胡建平, 孟存喜. 基于分步编码改进遗传算法的铁路智能选线[J]. 西南交通大学学报, 2013, 26(5): 831-838. doi: 10.3969/j.issn.0258-2724.2013.05.008
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

基于分步编码改进遗传算法的铁路智能选线

doi: 10.3969/j.issn.0258-2724.2013.05.008
基金项目: 

国家自然科学基金资助项目(51378512)

交通运输建设科技项目(20113187851460)

Intelligent Railway Aignment Optimization Based on Stepwise Encoding Genetic Algorithm

  • 摘要: 为实现铁路三维空间线形的智能优化,建立了综合考虑建设和运营费用、环境影响代价及线路约束的线形优化模型;针对平面、纵面控制点分别给出了分布方法,并构建了由平面交点偏移距、平曲线半径、变坡点标高3个基因片段组成的基因序列.在此基础上,提出了平面-纵面-完整基因的分布编码方法,设计了相应的多种交叉、变异算子实现线路方案的进化.实例分析表明:本方法克服了现有遗传算法产生的线路方案平、竖曲线重叠的缺陷;可生成满足线路约束条件且综合费用较省的线路方案群,最优的智能选线方案比人工定线方案综合费用节省了6.5%.

     

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出版历程
  • 收稿日期:  2012-09-26
  • 刊出日期:  2013-10-25

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