Optimizing Harris Hawks Behavior: The HHO Algorithm

The Harris Hawks Optimization (HHO) algorithm, inspired by the cooperative hunting behavior of Harris hawks, has emerged as a powerful tool for solving complex optimization problems. The algorithm mimics the two-step hunting strategy of the hawks, balancing exploration and exploitation to converge towards the optimal solution efficiently.

During the exploitation phase, the algorithm refines its search by moving towards the prey using specific equations to update the hawk’s position. On the other hand, the exploration phase involves modifying the hawk’s position based on random values to steer away from the current optimal solution. These phases work in tandem to enhance the algorithm’s exploratory capabilities and improve its performance.

Compared to other optimization methods, such as MFO, PSO, GWO, DE, and WOA, HHO has shown superior performance due to its robust architecture and minimal parameterization requirements. The algorithm does not rely on derivative information, making it versatile and user-friendly for a variety of practical applications.

By utilizing the 3-fold mean R² score as the objective function and optimizing hyper-parameter configurations using HHO, researchers have been able to achieve balanced evaluations and enhance the algorithm’s generalization capability. The algorithm has been successfully applied to various scenarios, such as finding the highest peak in a mountainous region, demonstrating its efficiency in solving real-world problems.

Overall, the Harris Hawks Optimization algorithm offers a unique and effective approach to optimization, drawing inspiration from nature to tackle complex challenges with precision and speed. Its adaptability and performance make it a valuable tool for researchers and practitioners in the field of optimization.