基于自适应反馈调节因子的阿基米德优化算法

计算机科学 ›› 2022, Vol. 49 ›› Issue (8): 237-246.doi: 10.11896/jsjkx.210700150

基于自适应反馈调节因子的阿基米德优化算法

陈俊, 何庆, 李守玉

贵州大学大数据与信息工程学院贵阳 550025

收稿日期:2021-07-14 修回日期:2021-12-06 发布日期:2022-08-02
通讯作者: 何庆(qhe@gzu.edu.cn)
作者简介:(youngchen777@163.com)
基金资助:
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);贵州省教育厅青年科技人才成长项目(黔科合KY字[2016]124);贵州大学培育项目(黔科合平台人才[2017]5788);贵州省公共大数据重点实验室开放课题(2017BDKFJJ004);贵州省科技计划项目(黔科合基础-ZK[2021]一般335)

Archimedes Optimization Algorithm Based on Adaptive Feedback Adjustment Factor

CHEN Jun, HE Qing, LI Shou-yu

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).

摘要/Abstract

摘要： 针对基础阿基米德优化算法收敛速度慢、容易陷入局部最优的问题,文中提出了一种基于自适应反馈调节因子的阿基米德优化算法。首先,通过佳点集初始化种群,增强初始种群的遍历性,提高初始解的质量;其次,提出自适应反馈调节因子,平衡算法的全局探索与局部开发能力;最后,提出了莱维旋转变换策略,增加种群的多样性,以防止算法陷入局部最优。将所提算法与主流算法在14个基准测试函数以及部分CEC2014函数上进行30次比较实验,结果表明,所提算法的平均寻优精度、标准差以及收敛曲线均优于对比算法。同时将所提算法分别与对比算法在14个基准函数上进行Wilcoxon秩和检验,检验结果显示所提算法与对比算法的差异性显著。将所提算法应用于焊接梁设计问题,其相比原始算法提升了2%,验证了所提算法的有效性。

关键词: 阿基米德优化算法, 佳点集, 莱维飞行, 旋转变换算子, 自适应反馈调节因子

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

中图分类号:

TP301.6

陈俊, 何庆, 李守玉. 基于自适应反馈调节因子的阿基米德优化算法[J]. 计算机科学, 2022, 49(8): 237-246. https://doi.org/10.11896/jsjkx.210700150

CHEN Jun, HE Qing, LI Shou-yu. Archimedes Optimization Algorithm Based on Adaptive Feedback Adjustment Factor[J]. Computer Science, 2022, 49(8): 237-246. https://doi.org/10.11896/jsjkx.210700150

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