精英反向学习与黄金正弦优化的HHO算法  

Elite Opposition-Based Learning Golden-Sine Harris Hawks Optimization

作　　者：郭雨鑫 刘升[1] 高文欣 张磊[1] GUO Yuxin;LIU Sheng;GAO Wenxin;ZHANG Lei(School of Management,Shanghai University of Engineering Science,Shanghai 201620,China)

机构地区：[1]上海工程技术大学管理学院,上海201620

出　　处：《计算机工程与应用》2022年第10期153-161,共9页Computer Engineering and Applications

基　　金：国家自然科学基金(61673258);上海市自然科学基金(19RZ1421600)。

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。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.

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 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]