Open AccessAn improved spotted hyena optimizer for PID parameters in an AVR system
Author(s) -
Guo Zhou,
Jie Li,
Zhonghua Tang,
Qifang Luo,
Yongquan Zhou
Publication year - 2020
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2020211
Subject(s) - pid controller , hyena , nonlinear system , control theory (sociology) , convergence (economics) , position (finance) , mathematics , computer science , mathematical optimization , artificial intelligence , engineering , control (management) , control engineering , physics , economics , temperature control , ecology , finance , quantum mechanics , biology , economic growth
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.