Computer Science ›› 2022, Vol. 49 ›› Issue (8): 237-246.doi: 10.11896/jsjkx.210700150

• Artificial Intelligence • Previous Articles     Next Articles

Archimedes Optimization Algorithm Based on Adaptive Feedback Adjustment Factor

CHEN Jun, HE Qing, LI Shou-yu   

  1. College of Big Data & Information Engineering,Guizhou University,Guiyang 550025,China
  • Received:2021-07-14 Revised:2021-12-06 Published:2022-08-02
  • About author:CHEN Jun,born in 1996,postgraduate.His main research interests include evolutionary computation and deep lear-ning.
    HE Qing,born in 1982,Ph.D,associate professor.His main research interests include big data application and evolutionary computation.
  • Supported by:
    Guizhou Province Science and Technology Plan Project Major Special Project(Qiankehe Major Special Project Word [2018] 3002,Qiankehe Major Special Project Word [2016] 3022),Guizhou Provincial Education Department Young Science and Techno-logy Talent Growth Project(Qiankehe KY Word [2016] 124),Guizhou University Cultivation Project(Qiankehe Platform Talent [2017]5788),Guizhou Provincial Public Big Data Key Laboratory Open Project (2017BDKFJJ004) and Guizhou Science and Technology Plan Project(Qiankehe Foundation-ZK[2021] General 335).

Abstract: Aiming at the problem of slow convergence speed of basic Archimedes optimization algorithm and is easy to fall into local optimum,this paper proposes an Archimedes optimization algorithm based on adaptive feedback adjustment factor.Firstly,initializing the population through the good point set to enhance the ergodicity of the initial population and improve the quality of the initial solution.Secondly,an adaptive feedback adjustment factor is proposed to balance the global exploration and local deve-lopment capabilities of the algorithm.Finally,the Levy rotation transformation strategy is proposed,to increase the diversity of the population and prevent the algorithm from falling into a local optimum.Comparative experiments of the proposed algorithm and mainstream algorithms are carried on 14 benchmark functions and some CEC2014 functions for 30 times.The optimization results of the algorithm on the function show that the average optimization accuracy,standard deviation and convergence curve of the proposed algorithm are better than that of the comparison algorithm.At the same time,Wilcoxon rank sum test is performed on 14 benchmark functions between the proposed algorithm and comparison algorithms.The test results show that the proposed algorithm is significantly different from comparison algorithms.It will be applied to the design of welded beams,which is 2% higher than the original algorithm,which verifies the effectiveness of the proposed algorithm.

Key words: Adaptive feedback adjustment factor, Archimedes optimization algorithm, Good point set, Levy fight, Rotation transformation strategy

CLC Number: 

  • TP301.6
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