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

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.