[1]JIA Heming,LIU Qingxin,LIU Yuxiang,et al.Hybrid Aquila and Harris hawks optimization algorithm with dynamic opposition-based learning[J].CAAI Transactions on Intelligent Systems,2023,18(1):104-116.[doi:10.11992/tis.202108031]
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Hybrid Aquila and Harris hawks optimization algorithm with dynamic opposition-based learning

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 1 Page number: 104-116 Column: 学术论文—知识工程 Public date: 2023-01-05
Title:
Hybrid Aquila and Harris hawks optimization algorithm with dynamic opposition-based learning
Author(s):
JIA Heming1;  LIU Qingxin2;  LIU Yuxiang3;  WANG Shuang1;  WU Di4
1. School of Information Engineering, Sanming University, Sanming 365004, China;
2. School of Computer Science and Technology, Hainan University, Haikou 570228, China;
3. College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China;
4. School of Education and Music, Sanming University, Sanming 365004, China
Keywords:
Aquila optimizer; Harris Hawk optimization; dynamic opposition-based learning; hybrid optimization; benchmark function; tubular column design problem; car crash design problem; Wilcoxon rank-sum test
CLC:
TP301.6
DOI:
10.11992/tis.202108031
Abstract:
In recent years, optimization algorithms such as the Aquila optimizer (AO) and Harris hawks optimization (HHO) have been proposed. Although AO has strong global optimization capabilities, its convergence accuracy is low, and it is susceptible to local optimization. While the HHO algorithm has a strong local development capability, it suffers from flaws such as limited global exploration capabilities and slow convergence speed. Given the limitations of the original algorithms, this paper combines the two algorithms and proposes a hybrid Aquila and Harris Hawks algorithm with dynamic opposition-based learning. It introduces a dynamic opposition-based learning strategy and proposes a hybrid Aquila and Harris Hawks algorithm with dynamic opposition-based learning. First, a dynamic opposition-based learning strategy is introduced in the initialization phase to improve the initialization performance and convergence speed of the algorithm. Second, the hybrid algorithm retains the exploration mechanism of AO and the exploitation mechanism of HHO, which improves the algorithm’s optimization ability. The simulation experiment compares several classical opposition-based learning strategies using 23 benchmark functions and two engineering design problems to test the optimization performance of the hybrid algorithm. The results show that the hybrid algorithm with dynamic opposition-based learning has better convergence performance and can effectively solve engineering design problems.
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