Fruit fly optimization algorithm based on a novel fluctuation model and its application in band selection for hyperspectral image

Spectral band selection is an important operation in the field of hyperspectral remote sensing. However, most of the techniques cannot satisfy the needs of efficiency and accuracy at the same time. In this paper, we present a novel spectral band selection method, fruit fly optimization algorithm (FOA). As yet, FOA has not been used to solve the problem of band selection in hyperspectral image. Through the study of the algorithm, we know that the advantages of FOA are its simple structure and fewer parameters to be adjusted, but the algorithm itself also has some drawbacks. Thus, we first analyze the shortcomings of the traditional FOA, and the corresponding proofs are given by mathematical method. Then, we separate the whole optimization process into two sub-processes, each of which plays a different role. According to the change of the current iteration information and historical optimum value, a fluctuation model is designed in sub-pro1, and its validity is analyzed and validated theoretically and experimentally. In sub-pro2, a control factor is defined to guide the change rate of the step size. These two sub-processes have their own emphasis, and they cooperate with each other, taking into account the global and local optimization capabilities of the algorithm. The test results on 26 benchmark functions also prove that the proposed algorithm is superior to various state-of-art comparison algorithms. Finally, we introduce the proposed algorithm into the band selection of hyperspectral remote sensing, the gratifying results indicate that the proposed algorithm has great potential in hyperspectral remote sensing field.