基于随机蛙跳算法的特征波长优选

Characteristic Wavelength Optimization Based on Random Frog Algorithm

在随机蛙跳算法的基础上,提出了一种改进的窗口随机蛙跳算法,即使用连续窗口代替单个波长点,所提算法提高了原蛙跳算法的寻优精度,降低了随机蛙跳算法的迭代次数,从而提高了算法的收敛速度。血液样本的实验结果表明,相较于全波段,建立的偏最小二乘法模型的预测均方根误差下降了47.9%,预测集相关系数Rp提高了4.07%,证明了所提算法的有效性。通过对3种主流算法及改进随机蛙跳算法筛选出的特征波长建立回归分析模型,证明了改进算法在特征波长选择上的优越性。

Based on the random frog algorithm, a window-based random frog algorithm is proposed. With a continuous window instead of a single wavelength point, the proposed algorithm improves the optimization accuracy of the original random frog algorithm and reduces the iteration times of the algorithm, thus improving the convergence speed. The results of the blood samples show that compared with the full-band results, the root mean square error of prediction(RMSEP) of the built partial least square model decreases by 47.9%, and the correlation coefficient of the prediction set, Rp, increases by 4.07%, proving the validity of the proposed algorithm. The regression analysis of the characteristic wavelengths selected by the three mainstream algorithms and the window-based random frog algorithm is carried out, which demonstrates the superiority of the improved algorithm in selecting the characteristic wavelength.