MRT列车运行模拟模型的多目标改进遗传算法  

Improved Multi-objective GA for MRT Train Operation Simulation Model

作　　者：石红国[1] 彭其渊[1] 郭寒英[2] 

机构地区：[1]西南交通大学交通运输学院,四川成都610031 [2]西华大学交通与汽车工程学院,四川成都610039

出　　处：《西南交通大学学报》2006年第5期658-662,共5页Journal of Southwest Jiaotong University

基　　金：高等学校博士点创新基金资助项目(080203)

摘　　要：为了求解城市快速交通(MRT)列车运行模拟模型,寻找最优的列车运行控制曲线,构造了多目标改进遗传算法.以列车运行过程中工况转换点为基因编码依据,以多个基因构成一个染色体代表一个控制方案,从而形成初始种群;根据列车运行控制的停站误差、时分误差和能耗等目标要求设计适应值函数;通过个体有效性检查保证选择、交叉和变异过程中新个体的有效性,并在各算子中加入保优算子,使新种群不淘汰上一代最优个体.实例计算表明,与多质点优化模型相比,在一定的误差范围内,遗传算法能够减少能耗10%以上,并能提供大量次优解,具有明显的优化效果.In order to solve an MRT（mass rapid transportation） train operation simulation model and obtain the optimum operation curve, an improved multi-objective GA （genetic algorithm） was proposed. In this improved GA, gene encoding is based on train control shift position, and a chromosome composed of genes represents a train operation plan to produce the original population. The fitness function includes the objectives of train control, such as stop deviation, time deviation and energy consumption. Every individual is checked with some rule before accepted as new population in course of selection, crossover and mutation, and each algorithm contains elitist reservation in the improved GA. In addition, the proposed GA was tested through a comparison with the multi-particle model by using a case. The result shows that the improved GA can save energy above 10% and give many alternative train control schemes.

关 键 词：交通运输规划 列车运行模拟 多质点优化模型 多目标改进遗传算法 

分 类 号：U231.6[交通运输工程—道路与铁道工程]