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An improved spotted hyena optimizer for PID parameters in an AVR system

1 Department of Science and Technology Teaching, China University of Political Science and Law, Beijing 100088, China
2 College of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China
3 Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence, Nanning 530006, China
4 Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning 530006, China

Special Issues: Application of Soft Computing

In this paper, an improved spotted hyena optimizer (ISHO) with a nonlinear convergence factor is proposed for proportional integral derivative (PID) parameter optimization in an automatic voltage regulator (AVR). In the proposed ISHO, an opposition-based learning strategy is used to initialize the spotted hyena individual's position in the search space, which strengthens the diversity of individuals in the global searching process. A novel nonlinear update equation for the convergence factor is used to enhance the SHO's exploration and exploitation abilities. The experimental results show that the proposed ISHO algorithm performed better than other algorithms in terms of the solution precision and convergence rate.
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Keywords spotted hyena optimizer; opposition-based learning; nonlinear convergence factor; PID parameter optimization; metaheuristic

Citation: Guo Zhou, Jie Li, Zhonghua Tang, Qifang Luo, Yongquan Zhou. An improved spotted hyena optimizer for PID parameters in an AVR system. Mathematical Biosciences and Engineering, 2020, 17(4): 3767-3783. doi: 10.3934/mbe.2020211

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