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山东大学学报 (工学版) ›› 2023, Vol. 53 ›› Issue (2): 11-22.doi: 10.6040/j.issn.1672-3961.0.2022.161

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融合Jaya高斯变异的自适应乌鸦搜索算法

黄华娟1,程前2,韦修喜1*,于楚楚2   

  1. 1.广西民族大学人工智能学院, 广西 南宁 530006;2.广西民族大学电子信息学院, 广西 南宁 530006
  • 收稿日期:2022-04-21 出版日期:2023-04-22 发布日期:2023-04-21
  • 作者简介:黄华娟(1984— ),女,广西崇左人,副教授,博士,主要研究方向为机器学习和数据挖掘. E-mail:hhj-025@163.com. *通信作者简介:韦修喜(1980— ),男,广西百色人,副教授,主要研究方向为计算智能和机器学习. E-mail:weixiuxi@163.com
  • 基金资助:
    国家自然科学基金项目(62266007,61662005);广西自然科学基金项目(2021GXNSFAA220068,2018GXNSFAA294068)

Adaptive crow search algorithm with Jaya algorithm and Gaussian mutation

HUANG Huajuan1, CHENG Qian2, WEI Xiuxi1*, YU Chuchu2   

  1. 1. College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, Guangxi, China;
    2. College of Electronic Information, Guangxi Minzu University, Nanning 530006, Guangxi, China
  • Received:2022-04-21 Online:2023-04-22 Published:2023-04-21

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摘要： 针对标准乌鸦搜索算法存在收敛速度慢、寻优精度低、位置更新具有盲目性的不足,提出一种融合Jaya高斯变异的自适应乌鸦搜索算法(adaptive crow search algorithm with Jaya algorithm and Gaussian mutation, GMJCSA)。通过高斯变异优化全局最优个体和自适应步长的合理变化,提高算法的收敛能力和寻优精度。在引导者发现自己被跟随的情况下引入Jaya算法,克服位置更新具有盲目性的不足。将GMJCSA用于16个基准函数优化和减速器设计问题,与其他智能算法进行试验对比,GMJCSA能取得更好的解。试验结果表明,GMJCSA对于函数优化和减速器设计问题能够较好地寻优求解,总体性能良好。

关键词: 乌鸦搜索算法, 高斯变异, Jaya算法, 工程优化, 函数优化

中图分类号: 

  • TP391
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