Hybrid Artificial Rabbits Optimization and Bat Algorithm for Global Optimization Problems

Abstract

Metaheuristic algorithms are widely used to solve complex global optimization problems. However, their performance depends heavily on maintaining an effective balance between exploration and exploitation. This study proposes a novel hybrid metaheuristic algorithm, termed AROBAT, by integrating the Artificial Rabbits Optimization (ARO) algorithm with the Bat Algorithm (BAT). This hybridisation strategy preserves the inherent exploration-exploitation transition mechanism of ARO while enhancing the stochastic search capability by incorporating the random-walk operator derived from BAT. To evaluate the effectiveness of this approach, extensive experiments were conducted using standard CEC benchmark functions, and the results were compared with those of the original ARO and BAT algorithms. The findings demonstrate that AROBAT consistently achieves superior or competitive performance across the majority of benchmark functions, indicating improved convergence speed and robustness. These results confirm that the proposed hybrid framework effectively alleviates premature convergence and stagnation in local optima, highlighting its potential as a reliable optimization tool for complex, high-dimensional problems. © 2026 IEEE.