Efficient Grey Wolf: High-Performance Optimization for 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 exploration and local exploitation. These innovations improve search efficiency and optimization precision while maintaining a lightweight computational footprint. Experimental evaluations on 16 benchmark functions demonstrate 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, further supported by SHAP-based interpretability analysis.