An improved hybrid Aquila Optimizer and Harris Hawks Optimization for global optimization

被引:31
|
作者
Wang, Shuang [1 ]
Jia, Heming [1 ]
Liu, Qingxin [2 ]
Zheng, Rong [1 ]
机构
[1] Sanming Univ, Sch Informat Engn, Sanming 365004, Fujian, Peoples R China
[2] Hainan Univ, Sch Comp Sci & Technol, Haikou 570228, Hainan, Peoples R China
关键词
aquila optimizer  harris hawks optimization; hybrid algorithm; representative-based hunting; opposition-based learning; ALGORITHM; EVOLUTION;
D O I
10.3934/mbe.2021352
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
This paper introduces an improved hybrid Aquila Optimizer (AO) and Harris Hawks Optimization (HHO) algorithm, namely IHAOHHO, to enhance the searching performance for global optimization problems. In the IHAOHHO, valuable exploration and exploitation capabilities of AO and HHO are retained firstly, and then representative-based hunting (RH) and opposition-based learning (OBL) strategies are added in the exploration and exploitation phases to effectively improve the diversity of search space and local optima avoidance capability of the algorithm, respectively. To verify the optimization performance and the practicability, the proposed algorithm is comprehensively analyzed on standard and CEC2017 benchmark functions and three engineering design problems. The experimental results show that the proposed IHAOHHO has more superior global search performance and faster convergence speed compared to the basic AO and HHO and selected state-of-the-art meta heuristic algorithms.
引用
收藏
页码:7076 / 7109
页数:34
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