A Novel Hybrid Grey Wolf Differential Evolution Algorithm
Neural and Evolutionary Computing
2025-10-14 v4 Systems and Control
Systems and Control
Applied Physics
Computational Physics
Abstract
Grey wolf optimizer (GWO) is a nature-inspired stochastic meta-heuristic of the swarm intelligence field that mimics the hunting behavior of grey wolves. Differential evolution (DE) is a popular stochastic algorithm of the evolutionary computation field that is well suited for global optimization. In this part, we introduce a new algorithm based on the hybridization of GWO and two DE variants, namely the GWO-DE algorithm. We evaluate the new algorithm by applying various numerical benchmark functions. The numerical results of the comparative study are quite satisfactory in terms of performance and solution quality.
Cite
@article{arxiv.2507.03022,
title = {A Novel Hybrid Grey Wolf Differential Evolution Algorithm},
author = {Ioannis D. Bougas and Pavlos Doanis and Maria S. Papadopoulou and Achilles D. Boursianis and Sotirios P. Sotiroudis and Zaharias D. Zaharis and George Koudouridis and Panagiotis Sarigiannidis and Mohammad Abdul Matint and George Karagiannidis and Sotirios K. Goudos},
journal= {arXiv preprint arXiv:2507.03022},
year = {2025}
}
Comments
19 pages, 32 figures, journal