城市轨道车辆阻力公式经验参数文化基因优化算法的研究

Research on memetic optimization algorithm of empirical parameter for resistance formula of urban rail vehicles

为了解决城市轨道车辆阻力公式经验参数不易精确求解的问题,提出了一种改进的文化基因优化算法。首先,基于城市轨道车辆运行阻力经验公式和实际的运行数据,建立了城市轨道车辆运行阻力经验参数最优化问题的数学模型。为提升算法性能以提高求解精度,结合了遗传算法全局搜索能力强与粒子群算法收敛速度快的特点,进行优势互补,构造了一种混合算法以便于全局搜索。其次,结合方程组求解法求解速度快和爬山法局部搜索能力强的特点,构造了一种混合算法以便于局部搜索。最后,在MATLAB 2010a GUI平台下采用几种不同的经验参数辨识算法和优化算法进行仿真实验。仿真结果表明,在相同条件下改进的文化基因优化算法能够寻到更精确的阻力公式经验参数。

In order to solve the problem that the resistance formula of urban rail vehicle is not easy to be optimized accurately,this paper proposed an improved memetic algorithm. Firstly,based on the empirical formula of urban rail vehicle running resistance and the actual running data,it established a mathematical model of the optimization problem of urban rail vehicle running resistance. In order to improve the performance of the optimization algorithm,it combined the strong global search ability of the genetic algorithm with the quick convergence speed of the particle swarm algorithm. They were the advantages of complementary. Correspondingly,it constructed a hybrid algorithm to facilitate global search. Secondly,combining the high solving speed of equations and the strong local search ability of mountain climbing method,it established a hybrid algorithm to facilitate local search. Finally,the simulation experiments were carried out by using several different empirical parameter identification algorithms and optimization algorithms on the MATLAB 2010 a GUI platform. The simulation results show that the proposed algorithm can find more accurate empirical parameters of the resistance formula.