Mind evolutionary bat algorithm and its application to feature selection of mixed gases infrared spectrum

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.