Fire Hawk Optimizer: a novel metaheuristic algorithm

Abstract This study proposes the Fire Hawk Optimizer (FHO) as a novel metaheuristic algorithm based on foraging behavior of whistling kites, black kites and brown falcons. These birds are termed Hawks considering specific actions they perform to catch prey in nature, specifically by means setting fire. Utilizing proposed algorithm, numerical investigation was conducted 233 mathematical test functions with dimensions 2–100, 150,000 function evaluations were performed for optimization purposes. For comparison, total ten different classical new algorithms utilized alternative approaches. The statistical measurements include best, mean, median, standard deviation 100 independent runs, while well-known analyses, such Kolmogorov–Smirnov, Wilcoxon, Mann–Whitney, Kruskal–Wallis, Post-Hoc analysis, also conducted. obtained results prove that FHO exhibits better performance than compared from literature. In addition, two latest Competitions Evolutionary Computation (CEC), CEC 2020 bound constraint problems real-world including mechanical engineering design problems, considered evaluation which further demonstrated superior capability optimizer over other is evaluated dealing real-size structural frames 15 24 stories method outperforms previously developed metaheuristics.