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Date Published: 16/06/2025
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DOI: https://doi.org/10.52714/dthu.14.8.2025.1551
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Vol. 14 No. 8 (2025): Natural Sciences Issue (Vietnamese)
Section
Natural Sciences Issue (Vietnamese)
How to Cite
Pham, N., Nguyen, T. T. H., & Dinh, N. T. N. (2025). Improving the quality of the grey wolf algorithm and genetic algorithm through tornado local search. Dong Thap University Journal of Science, 14(8), 50-66. https://doi.org/10.52714/dthu.14.8.2025.1551

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Improving the quality of the grey wolf algorithm and genetic algorithm through tornado local search

Nguyen Pham1, Thi Thu Hien Nguyen1, Nguyen Trong Nghia Dinh1,
1 Department of Information Technology, Ho Chi Minh City University of Industry and Trade, VietNam

Main Article Content

Abstract

This paper investigates the enhancement of the Grey Wolf Optimizer (GWO) by integrating it with the Genetic Algorithm (GA) and an additional tornado local search step to improve optimization performance. GWO is inspired by the hunting behavior of grey wolves, while GA is an evolutionary algorithm that simulates the process of natural selection. This hybrid approach leverages the exploitation capability of GWO, the exploration strength of GA, and the refinement potential of local search. Experiments on standard benchmark functions demonstrate that the GWO-GA approach significantly improves both accuracy and convergence speed compared to traditional methods. This novel approach to optimization promises applications across various fields. Future research includes optimizing algorithm parameters and testing on more complex, real-world optimization problems.

Keywords

Combination optimization, genetic algorithm, Grey Wolf Optimizer, optimal performance, tornado local search

Article Details

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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