Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (10): 153-161.DOI: 10.3778/j.issn.1002-8331.2011-0321

• Pattern Recognition and Artificial Intelligence • Previous Articles     Next Articles

Elite Opposition-Based Learning Golden-Sine Harris Hawks Optimization

GUO Yuxin, LIU Sheng, GAO Wenxin, ZHANG Lei   

  1. School of Management, Shanghai University of Engineering Science, Shanghai 201620, China
  • Online:2022-05-15 Published:2022-05-15



  1. 上海工程技术大学 管理学院,上海 201620

Abstract: The basic Harris hawks optimization(HHO) is easy to fall into local optimal value and has low convergence precision as well as slow convergence speed. An elite opposition-based learning golden-sine Harris hawks optimization(EGHHO) is proposed. Elite opposition-based learning mechanism is used to improve the diversity and quality of the population, improve the global optimization capability and convergence precision. Golden-sine algorithm is introduced to improve the way that Harris hawk hunts prey, effectively reducing the search space, reducing the convergence time and enhancing the ability of local mining. By solving multiple unimodal, multi-modal and high-dimensional test functions, the results show that the EGHHO algorithm, which combines the two strategies, has a stronger ability to jump out of local extremes, as well as higher optimization precision and speed.

Key words: Harris hawks optimization algorithm, elite opposition-based learning, golden-sine algorithm, high-dimensional optimization

摘要: 针对基本哈里斯鹰优化算法(Harris hawks optimization,HHO)易陷入局部最优值、收敛精度低和收敛速度慢的问题,提出融合精英反向学习与黄金正弦算法的哈里斯鹰优化算法(elite opposition-based learning golden-sine Harris hawks optimization,EGHHO)。融入精英反向学习机制,提高种群多样性和种群质量,提升算法全局寻优性能和收敛精度;融入黄金正弦算法优化哈里斯鹰围捕猎物的方式,有效缩小搜索空间,减少算法收敛时间,增强算法局部开发能力。通过求解多个单模态、多模态和高维度测试函数进行算法之间的对比,结果表明,融合两种策略的EGHHO算法具有较强跳出局部极值的能力以及更高的寻优精度和寻优速度。

关键词: 哈里斯鹰优化算法, 精英反向学习, 黄金正弦算法, 高维优化