Efficient Grey Wolf Optimization: A High-Performance Optimizer with Reduced Memory Usage and Accelerated Convergence

This paper presents an efficient Grey Wolf Optimizer (EGWO) designed to address the limitations of the standard Grey Wolf Optimizer (GWO), focusing on reducing memory usage and accelerating convergence. The proposed method integrates Sinusoidal Mapping for enhanced population diversity and a Transverse- Longitudinal Crossover strategy to balance global explo- ration and local exploitation. These innovations improve search efficiency and optimization precision while main- taining a lightweight computational footprint. Experi- mental evaluations on 10 benchmark functions demon- strate EGWO’s superior performance in convergence speed, solution accuracy, and robustness. Its application to hyperparameter tuning of a Random Forest model for a housing price dataset confirms its practical utility, fur- ther supported by SHAP-based interpretability analysis.