本文選題：文化基因算法　切入點(diǎn)：遺傳算法　出處：《計(jì)算機(jī)應(yīng)用研究》2017年12期

【摘要】：為了解決城市軌道車(chē)輛阻力公式經(jīng)驗(yàn)參數(shù)不易精確求解的問(wèn)題,提出了一種改進(jìn)的文化基因優(yōu)化算法。首先,基于城市軌道車(chē)輛運(yùn)行阻力經(jīng)驗(yàn)公式和實(shí)際的運(yùn)行數(shù)據(jù),建立了城市軌道車(chē)輛運(yùn)行阻力經(jīng)驗(yàn)參數(shù)最優(yōu)化問(wèn)題的數(shù)學(xué)模型。為提升算法性能以提高求解精度,結(jié)合了遺傳算法全局搜索能力強(qiáng)與粒子群算法收斂速度快的特點(diǎn),進(jìn)行優(yōu)勢(shì)互補(bǔ),構(gòu)造了一種混合算法以便于全局搜索。其次,結(jié)合方程組求解法求解速度快和爬山法局部搜索能力強(qiáng)的特點(diǎn),構(gòu)造了一種混合算法以便于局部搜索。最后,在MATLAB 2010a GUI平臺(tái)下采用幾種不同的經(jīng)驗(yàn)參數(shù)辨識(shí)算法和優(yōu)化算法進(jìn)行仿真實(shí)驗(yàn)。仿真結(jié)果表明,在相同條件下改進(jìn)的文化基因優(yōu)化算法能夠?qū)さ礁_的阻力公式經(jīng)驗(yàn)參數(shù)。
[Abstract]:In order to solve the problem that the empirical parameters of the resistance formula of urban rail vehicles are not easy to be solved accurately, an improved cultural gene optimization algorithm is proposed.Firstly, based on the empirical formula of the running resistance of urban rail vehicle and the actual operation data, the mathematical model of the optimization problem of the empirical parameters of the operation resistance of the urban rail vehicle is established.In order to improve the performance of the algorithm and improve the accuracy of the algorithm, a hybrid algorithm is constructed to facilitate the global search by combining the advantages of the genetic algorithm and the fast convergence speed of the particle swarm optimization algorithm.Secondly, a hybrid algorithm is constructed to facilitate local search by combining the fast speed of solving equations and the strong local search ability of mountain climbing method.Finally, several kinds of empirical parameter identification algorithms and optimization algorithms are used to simulate on MATLAB 2010a GUI platform.The simulation results show that the improved cultural gene optimization algorithm under the same conditions can find more accurate empirical parameters of the resistance formula.
【作者單位】： 大連海事大學(xué)信息技術(shù)學(xué)院;內(nèi)蒙古民族大學(xué)機(jī)械工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(60574018)
【分類(lèi)號(hào)】：TP18
，

本文編號(hào)：1716784