Efficient Routing Method based on Hybrid ACO Algorithm for Flying Ad-Hoc Networks

In the realm of multi-Unmanned Aerial Vehicles (UAV) systems, UAVs establish a direct connection with a ground station either through satellite links or infrastructure partake in mutual communication. The challenge of predicting communication patterns among these UAVs encompasses an optimization endeavor: devising optimal routes for UAVs to reach their destinations, thereby reaping comprehensive monitoring benefits, all within predefined constraints. This research delves into the realm of multi-UAV optimization utilizing a combined genetic algorithm (GA) and ant colony optimization (ACO) approach, presenting a novel solution proposal. The central principle of the suggested hybrid approach involves the substitution of unfavorable GA population members with freshly generated individuals via an ant colony algorithm. This study introduces and details a hybrid (GA & ACO) methodology, leveraging various ACO and GA attributes by considering FANET (Flying Ad-hoc Network) parameters. Notably, an independent ACO component is present. Within the FANET context, the hybrid GA-ACO approach emerges as beneficial for both genetic algorithms and ant colony optimization. Consequently, the synergy of ACO and GA yields the paramount solution for enhancing the FANET ad hoc network.