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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 5
1386-1394

Improved Archimedes optimization algorithm by multi-strategy collaborative and its application

Luo Shihanga,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
DOI: 10.19734/j.issn.1001-3695.2021.10.0427

Abstract

In order to overcome the drawbacks of Archimedes optimization algorithm(AOA), such as weak global search ability, easily trapping into local optimum and prematurely converge, this paper put forward Archimedes optimization algorithm improved by multi-strategy collaborative(MAOA). Initially, it used the random Gaussian mutation strategy to increase the diversity of the population in the iterative process and strengthen the global search ability. Then, based on the randomness, ergodicity and diversity of multiple chaotic models, it introduced the local chaotic search strategy to expand the scope range of the chaotic space and enhance the local development capabilities of the algorithm. At the same time, it proposed a nonlinear dynamic density decreasing factor to coordinate the global exploration ability and local development ability of the algorithm. Finally, for the sake of increasing the diversity of the population during the iteration process, this paper adopted the golden sine strategy of the Lévy flight guidance mechanism to perturb the population position and improve the ability of the algorithm to jump out of the local optimum. Through simulation experiments on 12 benchmark functions and part of the CEC2014 function set, the results show that the proposed algorithm can overcome the shortcomings of AOA's weak global exploration ability and easy to fall into local optimality, and improve the accuracy and stability of AOA. In addition, the introduction of mechanical optimization design cases for testing and analysis will further verify the feasibility and applicability of MAOA on practical issues.

Foundation Support

国家自然科学基金资助项目(62166006)
贵州省科技计划项目重大专项资助项目(黔科合重大专项字［2018］3002)
贵州省公共大数据重点实验室开放课题(2017BDKFJJ004)
贵州省科学技术厅资助项目(黔科合基础-ZK［2021］一般335)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.10.0427
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 5
Section: Algorithm Research & Explore
Pages: 1386-1394
Serial Number: 1001-3695(2022)05-017-1386-09

Publish History

[2021-12-16] Accepted Paper
[2022-05-05] Printed Article

Cite This Article

罗仕杭, 何庆. 多策略协同改进的阿基米德优化算法及其应用 [J]. 计算机应用研究, 2022, 39 (5): 1386-1394. (Luo Shihang, He Qing. Improved Archimedes optimization algorithm by multi-strategy collaborative and its application [J]. Application Research of Computers, 2022, 39 (5): 1386-1394. )

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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