Improving Zebra Optimization Algorithm via Fitness‐Distance Balance Strategy: Application to <scp>AVR</scp> ‐ <scp>LFC</scp> System

ABSTRACT This study proposes the FDB‐ZOA algorithm, which is an improved version of the Zebra Optimization Algorithm (ZOA) with the Fitness‐Distance Balance (FDB) strategy to enhance the exploration and exploitation balance. The developed algorithm was tested on CEC2020 benchmark functions and compared with 13 different state‐of‐the‐art meta‐heuristic algorithms, including ZOA. The comparisons were supported by mean success, standard deviation, box plots, convergence curves, and Wilcoxon and Friedman tests; FDB‐ZOA demonstrated superior performance in all dimensions. Additionally, the algorithm's application potential has been demonstrated through parameter optimization of FOPID and FOPI‐FOPD controllers in AVR‐LFC systems, with results validated via time domain analysis, robustness tests, and OPAL‐RT‐based real‐time simulations. The findings obtained indicate that FDB‐ZOA is a strong candidate solution from both theoretical and practical perspectives.