干旱区生态环境知识资源中心(Arid): Namib beetle optimization algorithm: A new meta-heuristic method for feature selection and dimension reduction

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DOI	10.1002/cpe.6524
	Namib beetle optimization algorithm: A new meta-heuristic method for feature selection and dimension reduction
	Chahardoli, Meysam; Eraghi, Nafiseh Osati; Nazari, Sara
通讯作者	Chahardoli, M (corresponding author), Islamic Azad Univ, Arak Branch, Comp Engn Dept, Arak, Iran.
来源期刊	CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
ISSN	1532-0626
EISSN	1532-0634
出版年	2021-08
英文摘要	Today, large amounts of data are generated in various applications such as smart cities and social networks, and their processing requires a lot of time. One of the methods of processing data types and reducing computational time on data is the use of dimension reduction methods. Reducing dimensions is a problem with the optimization approach and meta-heuristic methods can be used to solve it. Namib beetles are an example of intelligent insects and creatures in nature that use an interesting strategy to survive and collect water in the desert. In this article, the behavior of Namib beetles has been used to collect water in the desert to model the Namib beetle optimization (NBO) algorithm. In the second phase of a binary version, this algorithm is used to select features and reduce dimensions. Experiments on CEC functions show that the proposed method has fewer errors than the DE, BBO, SHO, WOA, GOA, and HHO algorithms. In large dimensions such as 200, 500, and 1000 dimensions, the NBO algorithm of meta-heuristic algorithms such as HHO and WOA has a better rank in the optimal calculation of benchmark functions. Experiments show that the proposed algorithm has a greater ability to reduce dimensions and feature selection than similar meta-heuristic algorithms. In 87.5% of the experiments, the proposed method reduces the data space more than other compared methods.
英文关键词	dimension reduction feature selection meta-heuristic algorithm Namib beetle optimization (NBO) optimization
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000688265700001
WOS类目	Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS研究方向	Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/362901
作者单位	[Chahardoli, Meysam; Eraghi, Nafiseh Osati; Nazari, Sara] Islamic Azad Univ, Arak Branch, Comp Engn Dept, Arak, Iran
推荐引用方式 GB/T 7714	Chahardoli, Meysam,Eraghi, Nafiseh Osati,Nazari, Sara. Namib beetle optimization algorithm: A new meta-heuristic method for feature selection and dimension reduction[J],2021.
APA	Chahardoli, Meysam,Eraghi, Nafiseh Osati,&Nazari, Sara.(2021).Namib beetle optimization algorithm: A new meta-heuristic method for feature selection and dimension reduction.CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE.
MLA	Chahardoli, Meysam,et al."Namib beetle optimization algorithm: A new meta-heuristic method for feature selection and dimension reduction".CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE (2021).