基于改进遗传算法的列车运行曲线优化

传统遗传算法很早就在列车运行优化研究中得到了应用，但是由于种群中染色体进化方向的不确定性和局部搜索能力不足，导致收敛速度缓慢和求解质量低下。针对以上问题，本文提出一种改进型遗传算法，对列车运行曲线的生成进行研究。以列车运行能耗最小为优化目标，将行车安全、准点和精确停车等约束条件转化为惩罚函数，同时以工况序列为遗传个体进行求解，为加快种群收敛速度和提高解的质量，设计包含准点调整和局部搜索的种群进化方向引导机制。仿真结果表明，改进后的算法适用于多约束的列车运行优化问题，有效提升了收敛速度，优化结果相比于简单遗传算法和自适应遗传算法更加节能。

Optimization of Train Operation Profile Based on Improved Genetic Algorithm

The classic genetic algorithm has been used for the optimization of train operation long ago. However, due to the uncertainty of population evolution direction and insufficient local search ability, the rate of convergence is slow and the quality of solution is low. In this paper, an improved genetic algorithm is proposed to study the optimization of train operation profile. The optimization objective is to minimize the energy consumption of train operation. The constraints are transformed into penalty functions, such as traffic safety, punctuality and precise parking etc. In order to accelerate the population convergent rate and improve the solution quality, a new mechanism is designed, which can guide the evolution direction of the population, and the punctuality adjustment and local search are included in the new mechanism. The demonstrations show that the improved genetic algorithm is suitable for train operation profile optimization and can improve the convergence speed effectively. Moreover, it’s result is more energy saving than the classic genetic algorithm and the adaptive genetic algorithm.