## 1 Introduction
PSO, taking a cue from the study of flocking behavior, works well with continuous, smooth topographies but breaks down in providing for discrete decision making, e.g., dynamic switching between relay and sensor nodes. This study aims to fill these gaps by introducing a hybrid optimization framework that combines the global search capability of genetic algorithms and the adaptive precision characteristic of reinforcement learning. Our solution novelty lies in incorporating Q-learning within a GA's mutation mechanism so that drones can \"learn\" position offsets that improve coverage and connectivity. For instance, in a wildfire surveillance mission, fixed-wing drones fly around the fire perimeter, and multirotor drones are deployed close to evacuation paths. As the fire spreads, the RL component dynamically reconfigures drones to prioritize new hotspots, maintaining fleet connectivity. Such responsiveness is vital in ad hoc and mesh networks, where uncertainty in the environment--unexpected signal interference, obstacle emergence, or energy constraint--demands real-time adjustment. By reinforcing behavior that improves fitness metrics (e.g., expanding coverage without compromising connectivity), the hybrid method achieves a balance between exploration (via GAs' population diversity) and exploitation (via RL's targeted optimizations), preventing the stagnation and inefficiency of single approaches. The applications of this study are economic viability and sustainability. The key limiting factor in drone operation, energy consumption, is closely related to deployment patterns. Networks designed to reduce unnecessary displacements and trajectories can dramatically prolong service durations. In precision agriculture, drones tasked with the surveillance of crop health must cover whole fields without revisiting paths to avoid draining batteries. Similarly, solar-powered drones as aerial base stations require precise positioning to optimize sunlight exposure and signal strength--a dual purpose that cannot be addressed by static algorithms. The iterative refinement of the hybrid framework, according to real-time feedback (e.g., weather changes affecting solar intake), ensures sustained performance where traditional methods break down. Furthermore, the introduction of 5G and edge computing renders optimized drone networks even more necessary. Drones as mobile edge servers process data locally, reducing latency for applications like autonomous vehicle coordination or augmented reality. In urban areas, where signals are obstructed by buildings, drones must form mesh networks that route data dynamically through clear paths. Optimization here involves not just physical placement but also resource allocation--distributing bandwidth and computation loads to prevent bottlenecks. This paper is structured to address these challenges in a systematic manner. Following this introduction, Section 2 provides an overview of prior work in drone deployment optimization, with a focus on advancements in evolutionary algorithms, swarm intelligence, and reinforcement learning. Section 3 details the hybrid framework design, encompassing the multi-objective fitness function, Q-learning integration, and constraint-handling techniques for heterogeneous fleets. Section 4 provides experimental results over simulated and parameterized environments, comparing the hybrid method with pure GAs and PSO on metrics of coverage area, stability of connection, and speed of convergence. Section 5 considers the broad implications of our results, including disaster response, rural coverage, and smart cities, while Section 6 concludes with future possibilities, such as dynamic obstacle avoidance and the incorporation of real-world signal attenuation models.