## 2 Related Work
### Critical Limitations  
Despite the significant advances in UAV path planning and optimization, there are several important limitations among the existing approaches. Firstly, while evolutionary algorithms such as genetic algorithms and differential evolution (DE) excel at global exploration, their mutation operations are deterministic and non-adaptive to real-time environmental feedback. For instance, GA-based approaches like NSBKA [7] and combinatorial swarm optimizers [9] rely on pre-defined mutation rates, and, thus, can be less sensitive to dynamic threats or mission alteration, as noted in applications like wildfire monitoring or disaster response scenarios [2; 13; 31]. Similarly, hybrid methods such as MAHACO [28] and AQLPSO [29] integrate swarm intelligence and reinforcement learning but fail to synergize the population diversity of GAs with the focused exploitation of RL at the mutation level. Such patchwork hybridization results in a lack of balance between exploration and exploitation, particularly in energy-constrained missions [30; 31; 36].  
Connectivity maintenance in dynamic multi-UAV networks is less investigated. While approaches like dueling DDQN [25] and CEL [15] enhance coordination by employing connectivity-aware rewards or experience sharing, they cannot maintain persistent connections under abrupt reconfigurations like ad hoc network collapse from spreading fires or urban signal interference [33; 37]. Energy-efficient methods, such as E\({}^{2}\)M\({}^{2}\)CPP [13] and IDDQN [32], are battery-centric; however, they fail to consider connectivity metrics in optimization goals, resulting in coverage vs. connectivity compromises in high-risk mission operations. Cooperative systems, such as the USV-UAV coordination framework [38], are resilient in dynamic marine environments; however, they are application-specific and cannot be extended to other scenarios, such as urban surveillance or disaster response.  
Most state-of-the-art approaches have application-specific limitations. For instance, MOTLBO [18] is for optimizing herbicide spraying in agriculture, and IPKO [33] is for customized path planning for maritime rescue, but these methods are not generalizable to more general scenarios like urban surveillance or disaster response. Likewise, vehicle-UAV collaboration frameworks [14; 39] and Voronoi-based decomposition methods [19] enhance large-area coverage but presume static environmental conditions and do not account for the real-time adaptation to arising obstacles or dynamic threats [6; 20]. Finally, though risk-aware systems such as SPP [34] and third-party assessments [33] are safety-oriented, they use static threat maps, which reduces their utility in scenarios with evolving hazards, e.g., propagating wildfires or abrupt signal jamming [2; 35]. Effective coverage path planning methods, like the multi-resolution probabilistic method [40], enhance city monitoring but encounter scalability limitations in big or very dynamic environments. The global and local moth-flame optimization (GLMFO) algorithm [41] addresses collision avoidance and energy efficiency but lacks real-time learning mechanisms for integration, and, thus, shows limited adaptability to dynamic mission requirements.  
These gaps point towards the necessity of a coherent framework that adjusts dynamically to environmental uncertainty while optimizing coverage, connectivity, and energy efficiency. Our research addresses these issues by incorporating Q-learning into GAs' mutation process, allowing drones to learn context-aware modifications that maximize mission goals in real time.