基于改进遗传算法的ATO速度曲线研究

Research on ATO Speed Curves Based on Improved Genetic Algorithm

常用遗传算法研究ATO速度曲线,但其存在着局部搜索能力和全局收敛效果较差的缺点。针对此问题,提出一种基于自适应变异算子的变异策略,并结合精英保存策略使算法全局收敛。然后基于列车动力学和牵引制动模型,建立多目标优化模型,用于求解ATO速度曲线。结果表明:改进算法比标准遗传算法效果更优,生成的ATO速度曲线符合相应模式的牵引控制策略,且算法具有一定的参考价值。

Genetic algorithms are commonly used to study ATO speed curves,but they have the disadvantages of limited local search ability and poor global convergence.In this paper,aiming at solving these problems,a mutation strategy based on adaptive mutation operator is proposed,which,in combination with the elite saving strategy,enables the algorithm to converge globally.Then based on the model of train dynamics and traction braking,a multi-objective optimization model is established to calculate ATO speed curves.The results show that the improved algorithm is more effective than standard genetic algorithms,that the generated ATO speed curves conform to the traction control strategy of the corresponding mode,and that the algorithm provides reference value for other projects.