Author(s)

Mahmoud Khatab¹, Mohamed El-Gamel¹, Ahmed I. Saleh², Asmaa H. Rabie², Atallah El-Shenawy^1,3

¹Department of Mathematics and Engineering Physics, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
²Department of Computers and Control, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
³Department of Mathematics, Faculty of Science, New Mansoura University, Mansoura, Egypt.

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.

Grey Wolf Optimization (GWO), Metaheuristic Algorithm, Optimization Problems, Agents’ Positions, Leader Wolves, Optimal Fitness Values, Optimization Challenges

Khatab, M. , El-Gamel, M. , Saleh, A. , Rabie, A. and El-Shenawy, A. (2024) Optimizing Grey Wolf Optimization: A Novel Agents’ Positions Updating Technique for Enhanced Efficiency and Performance. Open Journal of Optimization, 13, 21-30. doi: 10.4236/ojop.2024.131002.