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

doi:10.1007/s12652-020-02226-1

HFCAS OpenIR

	Fruit fly optimization algorithm based on a novel fluctuation model and its application in band selection for hyperspectral image
	Ding, Guoshen 1,2; Qiao, Yanli1 ; Yi, Weining 1; Fang, Wei1 ; Du, Lili1
	2020-08-12
发表期刊	JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
ISSN	1868-5137
通讯作者	Du, Lili(maria.lily@163.com)
摘要	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.
关键词	Fruit fly optimization algorithm Subsection strategy Fluctuation model Band selection
DOI	10.1007/s12652-020-02226-1
关键词[WOS]	PARTICLE SWARM OPTIMIZATION ; FEATURE-EXTRACTION ; SPARSE
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[41601379]
项目资助者	National Natural Science Foundation of China
WOS研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Telecommunications
WOS记录号	WOS:000559302500002
出版者	SPRINGER HEIDELBERG
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.hfcas.ac.cn:8080/handle/334002/70717
专题	中国科学院合肥物质科学研究院
通讯作者	Du, Lili
作者单位	1.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Key Lab Opt Calibrat & Characterizat, Hefei 230031, Peoples R China 2.Univ Sci & Technol China, Hefei 230026, Peoples R China
第一作者单位	中科院安徽光学精密机械研究所
通讯作者单位	中科院安徽光学精密机械研究所
推荐引用方式 GB/T 7714	Ding, Guoshen,Qiao, Yanli,Yi, Weining,et al. Fruit fly optimization algorithm based on a novel fluctuation model and its application in band selection for hyperspectral image[J]. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING,2020.
APA	Ding, Guoshen,Qiao, Yanli,Yi, Weining,Fang, Wei,&Du, Lili.(2020).Fruit fly optimization algorithm based on a novel fluctuation model and its application in band selection for hyperspectral image.JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING.
MLA	Ding, Guoshen,et al."Fruit fly optimization algorithm based on a novel fluctuation model and its application in band selection for hyperspectral image".JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING (2020).