## 1 Introduction  
The rapid evolution of unmanned aerial vehicles or drones has revolutionized various domains, including disaster response, wildlife tracking, telecommunications, and precision agriculture. As the autonomous systems transition from research prototypes to critical infrastructure, deployment strategies for them have to grapple with one over-arching challenge: trade-offs among conflicting objectives like spatial coverage, network connectivity, energy efficiency, and responsiveness to dynamic environments. Classical optimization methods would separately optimize single metrics like surveillance coverage or hardware cost; real-world deployment, however, demands a combined framework to balance these conflicting goals. In the case of a rescue mission after a disaster, for instance, the drones should search debris to find survivors while also creating an ad hoc network to relay live feeds to the rescuers. Here, drones are performing two significant functions, the functions of harvesting data and also serving as a communication relay, which requires strategies for deployment where coverage is optimized and connectivity maintained. Thissecond function serves to emphasize the richness of modern-day drone networks where traditional static or single-shot methodologies are not suitable to address the dynamic and multi-objective nature of real-world problematics. Drone networks are increasingly vital to use cases requiring decentralized, dynamic topologies. In rural broadband access, drones serve as airborne base stations that create mesh networks to close the digital divide where the terrestrial infrastructure is limited. Mesh-enabled drone networks, in contrast to conventional cellular towers, reconfigure themselves dynamically in response to changing demand, environmental interference, or temporary population density surges--e.g., festivals or emergencies. Every drone in a mesh network becomes a forwarding node for the others, creating a self-healing mesh of connectivity. Scaling out such networks involves delicate trade-offs, however: drones too densely packed squander resources and curtail coverage, while drones too widely spaced invite dead spots and burst latency. It is the same with military operations: ad hoc drone networks facilitate real-time intelligence sharing between units operating in enemy territory. These networks must balance stealth (minimizing the minimum detectability) and signal strength without jamming or physical assault [1]. The stakes are high--a failed deployment could compromise mission success or lives. These examples reflect the growing utilization of drone networks where flexibility, resilience, and effectiveness cannot be sacrificed. Beneath these applications lies the issue of multi-objective optimization, wherein conflicting demands must be traded off. Coverage maximization, for instance, would generally involve dispersing drones far apart but at the expense of stretching inter-drone distances past the point of communications breakdown. On the other hand, clustering drones around a base station realizes praiseworthy connectivity at the expense of coverage, a compromise that is exacerbated in heterogeneous fleets comprising long-endurance fixed-wing and agile multirotors. Fixed-wing UAVs, through its favored patrolling of large perimeters, demand linear flight and higher altitudes, while multirotors enable stable, localized surveillance. The optimization of such hybrid fleets demands algorithms to consider disparate mobility patterns, energy profiles, and sensor packages. Current methods, like genetic algorithms and particle swarm optimization, work effectively in homogeneous environments but cannot handle these intricacies. GAs, which simulate natural selection to develop solutions over generations, become stuck in local optima for discontinuous search spaces or discrete variables (e.g., drone type assignment).