Mind evolutionary bat algorithm and its application to feature selection of mixed gases infrared spectrum
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摘要: 为了解决混合气体多组分间特征吸收峰相互重叠引起的特征选择困难问题,提出了新型红外光谱特征选择方法,并对该方法的性能进行了分析与评价.首先,充分结合思维进化计算的并行机制、异化操作与蝙蝠算法的局部搜索能力,设计了思维进化蝙蝠算法.接着,通过实验采集两个混合气体数据库,利用思维进化蝙蝠算法对其目标组分的特征峰进行筛选.然后,从算法的收敛速度和筛选出的特征峰两个方面,将思维进化蝙蝠算法与基本蝙蝠算法、遗传算法、粒子群优化算法及并行萤火虫群优化算法等进行比较.最后,讨论了思维进化蝙蝠算法与无信息变量消除法相结合对结果的影响.实验结果表明:CO的特征峰范围包括2 090~2 110 cm-1和2 115~2 125 cm-1,共包含32个波长点;N2O的特征峰范围为2 225~2 250 cm-1,共包含26个波长点.利用筛选出的特征波长点建立的浓度反演模型,测试集均方根误差为0.155,决定系数可达0.908.实验结果表明:思维进化蝙蝠算法收敛速度快、全局搜索能力强,适用于存在重叠特征峰的混合气体的特征选择,对应的浓度反演模型的泛化性能也有显著提升.Abstract: Due to the fact that the characteristic peaks of multi-component of mixed gases have overlapping problem, it was hard to implement feature selection for each target gas. To solve this problem, a novel feature selection method was introduced. First, by making full use of the parallel mechanism, dissimilation operator of mind evolutionary computation and local search ability of bat algorithm, the mind evolutionary bat algorithm was designed. Two different mixed gases databases werecollected to validate the performance of proposed method. Then, from the aspects of convergence speed and characteristic peaks, the comparison with basic bat algorithm, genetic algorithm, particle swarmoptimization and parallel glowworm swarm optimization algorithm was investigated. Finally, the influence of combination with uninformative variable elimination method was discussed. Experimental results show that the characteristic peaks of carbon monoxide include 2 090-2 110 cm-1 and 2 115-2 125 cm-1, which total have 32 wavelength points while the characteristic peaks of nitrogen oxide were in range from 2 225 to 2 250 cm-1, which total have 26 wavelength points. Considering the concentration retrieve model established with the selected characteristic peaks, the root mean squared error of prediction set was 0.155, and the determined coefficient can reach as high as 0.908. Experimental results show that the proposed method has the advantage of rapid convergence speed and well global search ability, which was adaptable to do the feature selection for those mixed gases with overlapping problem.
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Key words:
- feature selection /
- mindevolutionarycomputation /
- bat algorithm /
- mixedgases /
- infraredspectrum
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[1] [2] Hong Mingjian, Wen Quan, Wen Zhiyu. New near infraredwavel-ength selection algorithm based on Monte-Carlo method[J]. Acta Optica Sinica, 2010, 30(12): 3637-3642. (in Chinese)洪明坚,温泉,温志渝. 一种基于蒙特卡罗方法的近红外波长选择算法[J]. 光学学报, 2010, 30(12): 3637-3642. [3] Qin Yuhua, Ding Xiangqian, Gong Huili. High dimensional feature selection in near infrared spectroscopy classification[J]. Infrared and Laser Engineering, 2013, 42(5): 1355-1359. (in Chinese)秦玉华, 丁香乾, 宫会丽. 高维特征选择方法在近红外光谱分类中的应用[J]. 红外与激光工程, 2013, 42(5): 1355-1359. [4] [5] [6] Guo Tengxiao, Ding Xuequan, Dong Xiaoqiang, et al. New method of preprocessing IR remote sensing spectrum signals based on EMD[J]. Infrared and Laser Engineering, 2013, 42(12): 3196-3200. (in Chinese)郭腾霄, 丁学全, 董晓强, 等. 基于EMD的红外.测光谱信号预处理新方法[J]. 红外与激光工程, 2013, 42(12): 3196-3200. [7] [8] Chen Zhong, Zhang Liqing, Liu Heyang, et al. Successive projections algorithm and its application to selecting the wheat near-infrared spectral variables[J]. Spectroscopy and Spectral Analysis, 2010, 30(4): 949-952. (in Chinese)成忠, 张立庆, 刘赫扬, 等. 连续投影算法及其在小麦近红外光谱波长选择中的应用[J]. 光谱学与光谱分析, 2010, 30(4): 949-952. [9] [10] Zou Xiaobo, Zhao Jiewen, Povey M J W, et al. Variables selection methods in near-infrared spectroscopy[J]. Analytica Chimica Acta, 2010, 667: 14-32. [11] Yang Xinshe. A new metaheuristic bat-inspired algorithm[J]. Nature Inspired Cooperative Strategies for Optimization, 2010, 284: 65-74. [12] [13] [14] Wang Chuanlong, Xie Keming. Convergence of a new evolutionary computing algorithm in continuous state space[J]. International Journal of Computer Math, 2002, 79(1): 27-37. [15] Mirjalili S, Lewis A. S-shaped versus V-shaped transfer functions for binary particle swarm optimization[J]. Swarm and Evolutionary Computation, 2013, 9(1): 1-14. [16] [17] Huang Guangbin, Zhu Qinyu, Siew C K. Extreme learning machine: theory and applications[J]. Neurocomputing, 2006, 70(1): 489-501. [18] [19] Liu Jiakun. High dimension artificial glowworm swarm optimization algorithm analysis application and research[D]. Nanning: Guangxi University for Nationalities, 2012. (in Chinese)刘佳昆.高维人工萤火虫群优化算法应用研究[D]. 南宁: 广西民族大学,2012. -
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