An Optimized Hybrid Bio-Inspired Framework for Machine Learning-Based 5G Resource Allocation

Esraa Taha Heussien; Walled Khalid Abdulwahab

doi:10.46604/peti.2026.16627

Authors

Esraa Taha Heussien Department of Computer Science, College of Computer Science and Information Technology, University of Kirkuk, Kirkuk, Iraq
Walled Khalid Abdulwahab Department of Computer Science, College of Computer Science and Information Technology, University of Kirkuk, Kirkuk, Iraq

DOI:

https://doi.org/10.46604/peti.2026.16627

Keywords:

5G networks, resource allocation, machine learning, grey wolf optimizer

Abstract

This study presents a lightweight hybrid framework for optimizing radio resource allocation in 5G networks, enabling high prediction accuracy with low computational complexity for edge deployment. A binary grey wolf optimization (BGWO) algorithm is used for feature selection, while a continuous GWO algorithm is used for hyperparameter adjustment of nine regression models trained using machine learning on a pre-processed public dataset for 5G networks. To ensure evaluation integrity, the dataset is segmented for training, validation, and testing before feature selection and expansion, preventing data leakage. Model performance is assessed using mean squared error (MSE) and the coefficient of determination (R²). The optimized decision tree achieves the best overall performance, improving R² from 46.32% to 99.44% and reducing MSE from 0.00470 to 0.00005, while random forest and support vector machine (SVM) exceed 99% R². These results demonstrate that the proposed framework achieves accurate and computationally efficient resource allocation for 5G networks.

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