## 4 Experimental Results
### Simulation Setup  
The simulations were conducted in three settings: wildfire monitoring, disaster response, and urban area monitoring. Each of the scenarios presented distinct challenges with regard to adapting to dynamic environmental conditions, connectivity constraints, and real-time adaptability needs. The setting for the wildfire monitoring was arranged with the objective of scaling up the deployment of drones to manage dynamic fire fronts effectively,  
Figure 2: Flow diagram of the proposed hybrid optimization framework.  
in addition to the provision of seamless interaction between the drones and the command center. The emergency response scenario demanded the drones change their locations in real time in order to achieve maximum coverage of the disaster sites, while keeping them in contact with emergency responders. Lastly, the urban surveillance scenario demanded coverage maximization with minimum interference from buildings and other city features.  
For the proper management of the drone fleet, the simulations were set with important default values. The drone fleet was made up of ten drones, including five fixed-wing drones and five multirotor drones. Both types of drones had their own operational limitations: fixed-wing drones had a 20 km distance they could travel, while multirotor drones had a maximum distance of 7 km they could travel. The genetic algorithm optimization was run for 150 generations on a population of 100. Reinforcement learning was implemented through the usage of Q-learning with a learning rate of 0.1, discount factor of 0.9, and exploration rate of 0.1. The action space was a discrete set of movements between \(-0.5\) km and 0.5 km to enable drones to adjust positions dynamically. All these values were parameterized, allowing the system to be easily tuned to various operating requirements. For fairness and reproducibility, benchmark algorithms--the genetic algorithm and particle swarm optimization--were executed with consistent parameter settings. Specifically, the GA was configured with a crossover rate of 0.8 and a mutation rate of 0.1. The PSO algorithm used a standard inertia weight of 0.5, and both cognitive and social coefficients were set to 1.5. All optimization methods shared the same population size (100) and number of generations (150). These parameter values were not selected arbitrarily; instead, they were tuned through a series of preliminary experiments to observe their effects on convergence speed, solution stability, and exploration-exploitation balance. This empirical tuning process enabled the control strategy to maintain consistent performance across simulation runs and ensured the hybrid method operated reliably under diverse mission scenarios.  
The primary actions that were utilized to quantify performance were the coverage area, connection stability, and convergence rate. The coverage area was calculated, taking into account the total area covered by the fleet with an attempt to reduce overlaps. The connection stability was examined on the basis of the number of total connections established between the drones and the average communicating distance between them. The convergence rate was measured based on how quickly each of the optimization algorithms converged to a convergent fitness value, i.e., how quickly it approached its convergent state, reflecting an optimization performance metric for approaching the best deployment state. Standalone GAs and PSO were considered as the benchmark comparison algorithms and were compared with the hybrid algorithm GA + RL to evaluate its viability in real-world applications.