[1]SONG Meijia,JIA Heming,LIN Zhixing,et al.Harris Hawks optimization algorithm based on nonlinear convergence factor and mutation quasi-reflected-based learning[J].CAAI Transactions on Intelligent Systems,2024,19(3):738-748.[doi:10.11992/tis.202205008]
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Harris Hawks optimization algorithm based on nonlinear convergence factor and mutation quasi-reflected-based learning

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 3 Page number: 738-748 Column: 学术论文—人工智能基础 Public date: 2024-05-05
Title:
Harris Hawks optimization algorithm based on nonlinear convergence factor and mutation quasi-reflected-based learning
Author(s):
SONG Meijia1;  JIA Heming2;  LIN Zhixing1;  LIU Qingxin3
1. Center of Network, Sanming University, Sanming 365004, China;
2. Department of Information Engineering, Sanming University, Sanming 365004, China;
3. School of Computer Science and Technology, Hainan University, Haikou 570228, China
Keywords:
Harris Hawks optimization; nonlinear convergence factor; quasi-reflected-based learning; quasi-inverse learning; chaotic mapping; engineering problems; meta-heuristic algorithms; swarm intelligence
CLC:
TP301.6
DOI:
10.11992/tis.202205008
Abstract:
Due to the shortcomings of the Harris Hawks optimization (HHO) algorithm, such as premature convergence, low optimization precision, and slow convergence speed, an improved HHO (IHHO) algorithm integrating nonlinear convergence factor and mutation quasi-reflection-based learning (QRBL) is proposed. First, circle chaotic mapping is introduced in the initialization stage to improve the diversity of the initialization population and the location and quality of the population. Second, the sigmoid nonlinear convergence factor is introduced to balance the ability of global exploration and partial exploitation. Finally, because the HHO algorithm easily falls into the local optimum, mutation QRBL is proposed to improve the vigor of the population and further improve the local convergence ability of the algorithm. The simulation experiments are conducted by applying 13 standard test functions and one classical engineering problem to the evaluation of the proposed algorithm. The results show that the convergence accuracy and the convergence speed of the IHHO algorithm are greatly improved, and IHHO is suitable for solving practical problems.
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