%0 Journal Article
%T Optimizing Grey Wolf Optimization: A Novel Agents¡¯ Positions Updating Technique for Enhanced Efficiency and Performance
%A Mahmoud Khatab
%A Mohamed El-Gamel
%A Ahmed I. Saleh
%A Asmaa H. Rabie
%A Atallah El-Shenawy
%J Open Journal of Optimization
%P 21-30
%@ 2325-7091
%D 2024
%I Scientific Research Publishing
%R 10.4236/ojop.2024.131002
%X Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm
that has gained popularity for solving optimization problems. In GWO, the
success of the algorithm heavily relies on the efficient updating of the
agents¡¯ positions relative to the leader wolves. In this paper, we provide a
brief overview of the Grey Wolf Optimization technique and its significance in
solving complex optimization problems. Building upon the foundation of GWO, we
introduce a novel technique for updating agents¡¯ positions, which aims to
enhance the algorithm¡¯s effectiveness and efficiency. To evaluate the
performance of our proposed approach, we conduct comprehensive experiments and
compare the results with the original Grey Wolf Optimization technique. Our
comparative analysis demonstrates that the proposed technique achieves superior
optimization outcomes. These findings underscore the potential of our approach
in addressing optimization challenges effectively and efficiently, making it a
valuable contribution to the field of optimization algorithms.
%K Grey Wolf Optimization (GWO)
%K Metaheuristic Algorithm
%K Optimization Problems
%K Agents¡¯ Positions
%K Leader Wolves
%K Optimal Fitness Values
%K Optimization Challenges
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=131814