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Algorithm Research & Explore
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1718-1723,1738

Mixed strategy to improve butterfly optimization algorithm

Ning Jieqionga,b
He Qinga,b
a. College of Big Data & Information Engineering, b. Guizhou Provincial Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China

Abstract

Aiming at the defects of butterfly optimization algorithm, such as low accuracy and easy to fall into local optimum, this paper proposed an improved butterfly optimization algorithm for mixed strategy. Firstly, in order to increase the diversity of initial individuals, this algorithm used Circle map to initialize the position of butterfly individuals. Secondly, in the phase of local search, it used dynamic switching probability to control the conversion between sine and cosine algorithm and butterfly optimization algorithm, so that it fully utilized a small number of butterflies to enhance the local development ability of the algorithm. Then, it introduced adaptive cotangent weight coefficients at global and local position updates to control the movement direction and distance of the next generation of butterflies and improve the convergence speed and accuracy of the algorithm. Finally, it introduced a dimensional-by-dimension mutation strategy to mutate the global optimal position, guide the population to evolve to the optimal position and avoid falling into the local optimum. The simulation results of eight benchmark functions show that the improved algorithm has better convergence performance. Compared with other improved algorithms, it has a certain competitiveness.

Foundation Support

贵州省科技计划项目重大专项项目(黔科合重大专项字[2018]3002,黔科合重大专项字[2016]3022)
贵州省公共大数据重点实验室开放课题(2017BDKFJJ004)
贵州省教育厅青年科技人才成长项目(黔科合KY字[2016]124)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.06.0171
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 6
Section: Algorithm Research & Explore
Pages: 1718-1723,1738
Serial Number: 1001-3695(2021)06-021-1718-06

Publish History

[2021-06-05] Printed Article

Cite This Article

宁杰琼, 何庆. 混合策略改进的蝴蝶优化算法 [J]. 计算机应用研究, 2021, 38 (6): 1718-1723,1738. (Ning Jieqiong, He Qing. Mixed strategy to improve butterfly optimization algorithm [J]. Application Research of Computers, 2021, 38 (6): 1718-1723,1738. )

About the Journal

  • 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.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.


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