Parameter optimization of PID controller based on an enhanced whale optimization algorithm for AVR system

This paper proposes an enhanced whale optimization algorithm based on a ranking-based mutation operator (EWOA) to stabilize the PID controller's parameters in the AVR system, and the intention is to arrive at the ideal objective function value by modifying the control parameters. The whale optimization algorithm is based on the humpback whale's bubble-net attacking behavior, which imitates shrinkage encircling prey, bubble-net attacking prey and random searching for prey to address the complicated optimization issue. The ranking-based mutation operator can maximize the selection probability, screen out the best search individual, eliminate premature convergence, promote the convergence speed and elevate the exploitation ability. The EWOA not only has substantial robustness and stability to strengthen the optimization efficiency and recognize the optimal solution but also combines exploration with exploitation to expand the convergence rate and calculation precision. The EWOA is contrasted with other algorithms to validate the practicability and usefulness. The experimental results demonstrate that the EWOA has a quicker convergence rate, higher computation precision, shorter execution time and greater stability, which is a remarkable and practical method for addressing the parameter optimization of the PID controller in the AVR system.