Statistical Performance Analysis of an Adaptive Honey Badger Optimization Algorithm on Benchmark Functions

This paper proposes the Extended Honey Badger Optimization (EHBO) algorithm, which improves the Honey Badger Algorithm by using an adaptive control parameter to balance exploration and exploitation. EHBO is tested on six benchmark functions and compared with PSO, DE, ABC, BA, and HBA using 30 independent runs. The results indicate that EHBO performs well with smaller variability, especially on multimodal functions. On Rastrigin, EHBO reached a mean fitness of 4.87E+01 (std 8.31E+00), performing better than PSO (5.33E+01, std 9.85E+00) and DE (3.96E+01, std 6.72E+00). On Ackley, EHBO obtained a mean fitness of 1.91E+01 (std 1.84E+00), showing smaller variability than HBA (2.12E-02, std 8.31E-03). Non parametric tests (Wilcoxon, Friedman) at a 95% confidence level verify the statistical significance of EHBO’s improvements. The proposed algorithm ensures stable convergence and less dependence on initial conditions, making it a trustworthy solver for complex optimization problems.