Optimizing Drone Deployment for Network Coverage Using a Hybrid Ant Colony Optimization and Reinforcement Learning Approach
The efficient deployment of drones to establish an effective communication network is a challenging problem in a variety of use cases, from disaster management to rural coverage. In this study, we present a hybrid optimization strategy using Ant Colony Optimization (ACO) an...
| Published in: | Havacılık ve Uzay Teknolojileri Dergisi |
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| Main Authors: | , |
| Format: | Article |
| Language: | English |
| Published: |
Turkish Air Force Academy
2025-07-01
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| Subjects: | |
| Online Access: | https://jast.hho.msu.edu.tr/index.php/JAST/article/view/635/443 |
| Summary: | The efficient deployment of drones to establish an effective communication network is a challenging problem in a variety of use cases, from disaster management to rural coverage. In this study, we present a hybrid optimization strategy using Ant Colony Optimization (ACO) and Deep Reinforcement Learning (DRL) for optimizing drone placement and mobility in a target geographic area. The proposed method leverages ACO's global search capability coupled with DRL's adaptive learning capability for optimizing network coverage and guaranteeing optimal connectivity among drones and a central hub. The hybrid technique is contrasted with a solo ACO approach, with the former exhibiting superior performance regarding coverage, connectivity, and deployment efficiency. |
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| ISSN: | 1304-0448 2148-1059 |