Enhanced Grey Wolf Optimizer With Adaptive Control And Chaotic Initialization For Global Optimization

This paper presents a novel enhancement to the Grey Wolf Optimizer (GWO) by integrating two key mechanisms: chaotic population initialization using the logistic map and adaptive parameter control through nonlinear decay. The proposed Enhanced Grey Wolf Optimizer (EGWO) aims to overcome common limitations of standard GWO, such as premature convergence and poor exploitation in high-dimensional search spaces. The chaotic initialization promotes early-stage diversity, while the adaptive strategy ensures a dynamic balance between exploration and exploitation. EGWO is evaluated across fifteen well-known benchmark functions in 30 dimensions. Compared to standard GWO, it achieves up to a 30% faster convergence and a 25% improvement in solution accuracy. Statistical tests confirm EGWO’s consistent superiority in both performance and robustness, making it a competitive algorithm for solving complex global optimization problems.