Improvement of Artificial Hummingbird Algorithm with Greedy Fitness–Distance Balance Method and Its Application to Load Frequency Control

This paper proposes an improved Artificial Hummingbird Algorithm (AHA) for solving numerical optimization problems. The greedy Fitness–Distance Balance (FDB) selection method is integrated into the guided, territorial, and migration search operators of the AHA. This establishes a balance between exploration and exploitation by enhancing AHA’s exploration capabilities. The performance of the proposed FDB-AHA is evaluated using CEC 2017 and 2020 problems which include single-mode, multi-mode, hybrid, and composition functions in search spaces of dimensions 30, 50, and 100. Furthermore, the results of the FDB-AHA are compared with 11 other advanced search algorithms using the Friedman statistical test method on the CEC 2017 and 2020 problems. As a result of these comparisons, the FDB-AHA was found to be the most effective method, while the AHA ranked ninth. These findings confirm the effectiveness of the improvements made in the AHA. Moreover, the FDB-AHA has been used to determine the optimal solutions for load frequency control in three different power systems. In this context, three-term (PID), five-term (FOPID), and six-term (FOPI-FOPD) controllers are optimized and compared with existing studies in the literature for time-domain analysis. Furthermore, load variation and robustness analysis are also performed. Finally, the practicality of the controllers optimized by FDB-AHA is verified using the real-time simulator OP5707. Simulation results show that FDB-AHA is a highly efficient algorithm for solving all optimization problems.