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首页> 外文期刊>International Journal of Innovative Computing Information and Control >TRAIN OPERATION STRATEGY OPTIMIZATION BASED ON IMPROVED GENETIC ALGORITHM
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TRAIN OPERATION STRATEGY OPTIMIZATION BASED ON IMPROVED GENETIC ALGORITHM

机译：基于改进遗传算法的列车运行策略优化

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摘要

The multi-objective optimization problem in train operation is a nonlinear control problem which is affected by many kinds of constraints and parameters. There are more than one or infinite Pareto solutions in the optimal solution set, so it is very difficult to obtain an accurate train control sequence. So, taking the energy consumption, punctuality and comfort as the optimization objectives, and combining the dynamic equation and constraint equation of urban rail train, the multi-objective optimization model of train operation process is established in this paper. At the same time, an adaptive Genetic Algorithm (GA) based on Directional Mutation (DM) and Gene Modification (GM) is proposed to solve the optimization model. In addition, the fusion distance is used to judge the existence of individual aggregation phenomenon at the late stage of iteration for GA, thereby inhibiting the local convergence. Both MATLAB simulation results and HIL (Hardware-in-the-Loop) simulation results show that the improved GA can quickly obtain an accurate control sequence of train operation, which has great reference value for the actual train operation control.
机译：列车操作中的多目标优化问题是一个非线性控制问题，受许多规约和参数的影响。在最佳解决方案集中有多个或无限的帕累托解决方案，因此非常难以获得准确的列车控制序列。因此，以优化目标为能源消耗，准时和舒适度，并结合城市火车列车的动态方程和约束方程，在本文中建立了列车运行过程的多目标优化模型。同时，提出了一种基于定向突变（DM）和基因改性（GM）的自适应遗传算法（GA）来解决优化模型。另外，融合距离用于判断Ga的迭代后期的个体聚集现象的存在，从而抑制局部收敛。 MATLAB仿真结果和HIL（循环）仿真结果表明，改进的GA可以快速获得准确的列车操作控制序列，对实际列车操作控制具有很大的参考值。

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