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山东大学学报 (工学版) ›› 2023, Vol. 53 ›› Issue (4): 93-103.doi: 10.6040/j.issn.1672-3961.0.2022.128

• 机器学习与数据挖掘 • 上一篇    

混合改进策略的阿奎拉鹰优化算法

刘庆鑫1,齐琦1*,贾鹤鸣2,李霓3,4   

  1. 1.海南大学计算机科学与技术学院, 海南 海口 570228;2.三明学院信息工程学院, 福建 三明 365004;3.海南师范大学数学与统计学院, 海南 海口 571158;4.海南师范大学数据科学与智慧教育教育部重点实验室, 海南 海口 571158
  • 发布日期:2023-08-18
  • 作者简介:刘庆鑫(1997— ),男,福建福州人,硕士研究生,主要研究方向为群体智能与图像分割. E-mail:qxliu@hainanu.edu.cn. *通信作者简介:齐琦(1978— ),男,北京人,副研究员,博士,主要研究方向为组合优化算法与机器学习. E-mail:qqi@hainanu.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(11861030);海南省自然科学基金资助项目(2019RC176,621RC511);福建省自然科学基金资助项目(2021J011128);海南省研究生创新科研课题资助项目(Qhys2021-190)

Aquila optimizer based on hybrid improved strategies

LIU Qingxin1, QI Qi1*, JIA Heming2, LI Ni3,4   

  1. 1. School of Computer Science and Technology, Hainan University, Haikou 570228, Hainan, China;
    2. School of Information Engineering, Sanming University, Sanming 365004, Fujian, China;
    3. School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, Hainan, China;
    4. Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, Haikou 571158, Hainan, China
  • Published:2023-08-18

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摘要： 针对阿奎拉鹰优化算法(Aquila optimizer, AO)收敛速度慢、易陷入局部最优且寻优精度较低等问题,提出混合改进策略的阿奎拉鹰优化算法(Aquila optimizer based on hybrid improved strategies, HH-SAO)。初始化阶段引入准反向学习策略,增强初始化种群多样性。引入正弦波随机策略,提高算法全局探索阶段随机性,提升算法全局寻优能力。利用哈里斯鹰算法(Harris hawks optimization, HHO)的4种攻击策略替换原AO算法的局部开发阶段策略,提高算法跳出局部极小值能力;引入能量缩减机制实现全局与局部阶段的动态转换,平衡算法全局探索和局部开发能力。仿真试验选取23个基准测试函数和1个经典工程设计问题进行性能测试,结果表明改进算法相较于其他流行算法具有更好的寻优能力和工程适用性。

关键词: 阿奎拉鹰优化算法, 哈里斯鹰优化算法, 准反向学习, 正弦波随机策略, 能量缩减机制

中图分类号: 

  • TP301.6
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