Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 12
3639-3643,3650

Somersault foraging seagull optimization algorithm for function optimization and feature selection

Xu Minga,b
Long Wena,c
Yang Yangb
a. Guizhou Key Laboratory of Big Data Statistics Analysis, b. School of Mathematics & Statistics, c. Key Laboratory of Economics System Simulation, Guizhou University of Finance & Economics, Guiyang 550025, China
DOI: 10.19734/j.issn.1001-3695.2022.05.0224

Abstract

The basic SOA has some drawbacks such as low solution accuracy, slow convergence rate, and easy to fall into local optima while solving complex function optimization problems. This paper proposed an improved SOA based on somersault foraging strategy(SFSOA). Firstly, the proposed SFSOA updated the positions of seagull individuals by using nonlinearly decrease strategy of control parameter A based on the inverted sigmoid function. This way improved individual solution quality and accelerates convergence speed of algorithm. Then, it introduced novel learning mechanism based on the somersault foraging strategy for the global best individual to increase the position diversity of seagull individual and avoid the algorithm falling into a local optimum at the lately stage. The proposed SFSOA was compared against the basic SOA, grey wolf optimizer(GWO), and improved SOA(ISOA) by using eight complex benchmark function optimization problems. Experimental results reveal that the proposed SFSOA has higher solution accuracy, faster convergence rate and stronger global search ability than its competitors and can effectively deal with complex function optimization problems. Finally, SFSOA is applied to solve the feature selection problems and the satisfactory results are obtained.

Foundation Support

国家自然科学基金资助项目(61463009)
贵州省自然科学基金资助项目(黔科合基础［2020］1Y012)
贵州省教育厅创新群体重大研究项目(黔教合KY字［2021］015)
贵州省大数据统计分析重点实验室开放课题(BDSA20190106)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.05.0224
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 12
Section: Algorithm Research & Explore
Pages: 3639-3643,3650
Serial Number: 1001-3695(2022)12-017-3639-05

Publish History

[2022-07-19] Accepted Paper
[2022-12-05] Printed Article

Cite This Article

徐明, 龙文, 羊洋. 用于函数优化和特征选择的翻筋斗觅食海鸥优化算法 [J]. 计算机应用研究, 2022, 39 (12): 3639-3643,3650. (Xu Ming, Long Wen, Yang Yang. Somersault foraging seagull optimization algorithm for function optimization and feature selection [J]. Application Research of Computers, 2022, 39 (12): 3639-3643,3650. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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