A Hybrid Optimization Framework for Dynamic Drone Networks: Integrating Genetic Algorithms with Reinforcement Learning

Abstract

The growing use of unmanned aerial vehicles (UAVs) in diverse fields such as disaster recovery, rural regions, and smart cities necessitates effective dynamic drone network establishment techniques. Conventional optimization techniques like genetic algorithms (GAs) and particle swarm optimization (PSO) are weak when it comes to real-time adjustment to the environment and multi-objective constraints. This paper proposes a hybrid optimization framework combining genetic algorithms and reinforcement learning (RL) to improve the deployment of drone networks. We integrate Q-learning into the GA mutation process to allow drones to adaptively adjust locations in real time under coverage, connectivity, and energy constraints. In the scenario of large-scale simulations for wildfire tracking, disaster response, and urban monitoring tasks, the hybrid approach performs better than GA and PSO. The greatest enhancements are 6.7% greater coverage, 7.5% less average link distance, and faster convergence to optimal deployment. The proposed framework allows drones to establish strong and stable networks that are dynamic in nature and adapt to dynamic mission demands with efficient real-time coordination. This research has important applications in autonomous UAV systems for mission-critical applications where adaptability and robustness are essential.