Dynamic Neighborhood-Based Grey Wolf Optimizer With Dimension Learning-Based Hunting And Lévy Flight

In this study, a modified variant of the Grey Wolf Optimizer (GWO) algorithm, integrating Lévy flight, Distance-Based Dynamic Neighborhood Strategy (DNDNS), and Dimension Learning-based Hunting (DLH) is proposed. This enhanced algorithm aims to address the inherent limitations of the traditional GWO, including its slow convergence rate, loss of population diversity, and predisposition towards local optima. The amalgamation of these strategies endows the proposed algorithm with improved exploration and exploitation capabilities, thereby bolstering its convergence towards global optima and streamlining the search process. A rigorous assessment carried out on a set of 23 commonly utilized benchmark functions evinces that the proposed variant of GWO consistently outperforms not only the just the traditional GWO but also other powerful modern optimization algorithms. The noteworthy performance of the enhanced GWO reinforces its potential as a promising solution for intricate optimization problems spanning diverse domains.