An Efficient Global Optimization-Based Grey Wolf Optimization Algorithm for Fast Convergence to Various Optimization Functions
| International Journal of Electrical and Electronics Engineering |
| © 2024 by SSRG - IJEEE Journal |
| Volume 11 Issue 10 |
| Year of Publication : 2024 |
| Authors : Dipak Patel, Bhavesh Patel, Prashant Modi, Shakti Patel, Rahul Shah |
10.14445/23488379/IJEEE-V11I10P105
How to Cite?
Dipak Patel, Bhavesh Patel, Prashant Modi, Shakti Patel, Rahul Shah, "An Efficient Global Optimization-Based Grey Wolf Optimization Algorithm for Fast Convergence to Various Optimization Functions," SSRG International Journal of Electrical and Electronics Engineering, vol. 11, no. 10, pp. 47-52, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I10P105
Abstract:
The Grey Wolf Optimizer (GWO), a bio-inspired metaheuristic algorithm, has gained prominence for solving complex optimization problems across various domains. Despite its advantages, the standard GWO algorithm often suffers from premature convergence and inefficacy in handling local optima, limiting its applicability for global optimization tasks. This research paper introduces an efficient GWO algorithm incorporating a novel adaptive search mechanism designed to overcome local optima entrapment issues and slow convergence rates inherent in the conventional GWO approach. This research analyses the behavior of the traditional GWO algorithm and identifies its key limitations in the exploration and exploitation phases. Then, a modified exploration technique is proposed with an adaptive exploitation method, dynamically adjusting the position update mechanism of wolves. The proposed modifications aim to sustain diversity in the search space and enhance the global search capability, thus accelerating convergence towards the global optimum and converging fast to the solution. Extensive experimental evaluations on several benchmark unimodal and multimodal functions demonstrate that the modified GWO algorithm significantly outperforms the original version and other contemporary optimization techniques regarding convergence speed, solution accuracy, and robustness against local optima. This research not only presents a viable solution to the limitations of the standard GWO but also contributes to the broader field of swarm intelligence, offering insights that could inspire further innovations in metaheuristic algorithms.
Keywords:
Global optima, Grey Wolf Optimization (GWO), Metaheuristic algorithm, Optimization problems, Swarm intelligence.
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