Yahia Mohsen Abu Saqer

Work place: Islamic University of Gaza/Faculty of IT, Gaza, P850, Palestine

E-mail: yahiasaqer@gmail.com

Website: https://orcid.org//0009-0007-4965-0468

Research Interests:

Biography

Yahia M. Abusaqer was born in Gaza, Palestine. He received a Bachelor's degree in computer science from the Islamic University of Gaza, Palestine, in 2022. He is currently pursuing a Master's degree in data science at the same university.He worked as a Teaching Assistant at the Islamic University of Gaza from August 2022 to October 2023, where he taught courses in assembly language, front-end development, back-end development, and freelancing online. His paper, "Exploring Arabic Sentiment Analysis: A Comparative Analysis of Techniques and Approaches," was accepted for oral presentation at the 11th International Conference on Advanced Technologies 2023, held in Istanbul, Turkey, from August 17 to August 19, 2023. His research interests include natural language processing, sentiment analysis, and applied machine learning.Mr. Abu Saqer was ranked second in his class during his undergraduate studies and is currently ranked first in his master's program.

Author Articles
ACO-QL: Enhancing ACO Algorithm for Routing in MANETs Using Reinforcement Learning

By Yahia Mohsen Abu Saqer Khalil Mohammed Eslayyeh Nasser Majed Abudalu Aiman A. Abusamra

DOI: https://doi.org/10.5815/ijem.2025.05.03, Pub. Date: 8 Oct. 2025

ACO-based routing protocols like AntHocNet have emerged as a solution for adaptive routing in MANETS. Likewise, deep Q-learning based protocols are suitable for complex and dynamic environments like MANETs and utilizing real time data for better decision-making. However, there is lack of studies in enhancing ACO-based protocols using Q-learning in a new hybrid protocol, and comparing it with the established ACO-based protocol AntHocNet.
By combining ACO’s strengths (eg. Multi-agent pathfinding and historical data creatd by pheromones) and combine it with key components of Q-learning, then we have a promising protocol ready to be compared with AntHocNet. Previous studies have explored integrating ML with MANET routing, but few of them, if any, have explored enhancing ACO using ML techniques. Therefore, we propose two new protocols: ACO-QL and ACO-DQN.
One uses Q-learning and the latter uses deep Q-learning. After conducting many experiments by running implementations of ACO-DQN, ACO-QL, and AntHocNet on a MANET simulation, we found out that AntHocNet is superior to ACO-DQN in terms of execution time, end-to-end delay, and path cost in most cases, but on the other hand ACO-DQN achieved better packet delivery ratio and throughput results. Meanwhile, ACO-QL consistently achieved lower packet delivery ratios than AntHocNet, and mostly matching AntHocNet’s performance in terms and of other metrics, making it a valid lightweight and faster alternative.

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