基于改进蝙蝠算法的红外光谱特征选择

特征选择是红外光谱定性与定量分析中的重要环节之一。为了解决传统特征选择方法可调参数多、收敛速度慢、精度低、易早熟等不足，对基本蝙蝠算法进行了离散化改进以适用于离散优化问题，同时结合Lvy飞行搜索策略，提出了一种新型的红外光谱特征选择算法。采用三个红外光谱数据集对提出的算法进行了验证，同时与遗传算法、模拟退火算法、无信息变量消除法等进行了比较分析。实验结果显示，该方法可以快速地搜索到全局最优值，能有效地提高波长选择的准确性和稳定性，被选择的波长物理、化学意义明确，采用选择的特征波段建立的定量模型优于用全谱建立的模型。同时，三个不同相态、不同光谱范围的数据集表明，所提出的算法具有较大的适用范围与实用价值。

Feature selection of infrared spectrum based on improved bat algorithm

Feature selection is an important part during the process of qualitative and quantitative analysis of infrared spectrum. In order to solve the disadvantage of traditional methods, such as multi-parameters, slow convergence, poor accuracy, prone to premature, etc., a novel feature selection algorithm was proposed, which combined the basic bat algorithm and Lvy flights search strategy. Meanwhile, due to the original version of bat algorithm was only suitbale for continuous problems, a binary version of bat algorithm was proposed. Three infrared spectrum datasets were used to check the performance of proposed method while the comparisons with traditional genetic algorithm, simulate anneal algorithm and uninformative variable elimination methods were also implemented. The experiment results show that, the proposed method can quickly find the global best combination of sub-intervals and improve the accuracy and stability of feature selection. More importantly, the selected wavenumbers have exactly physical meanings. Meanwhile, the generalized performance of the model established based on the selected wavenumbers was better than the whole spectral range. The tests on three different phases(solid, liquid and gas) and different spectral range indicated that, the proposed algorithm has a widely practical scope and value.