# Multi-Objective Optimized GPSR Intelligent Routing Protocol for UAV Clusters
## 6 Conclusions  
In this study, we focus on the environmental characteristics of UAV clusters and aim to develop an intelligent routing protocol that adapts to highly dynamic and large-scale networks. We have designed a multi-objective optimized GPSR routing protocol based on DDQN (DDQN-MTGPSR).  
Firstly, we addressed the mathematical modeling of the multi-objective routing optimization problem by integrating the multi-objective optimization mechanism of DDQN. The route forwarding process is transformed into a Markov decision process (MDP), and we achieve the modeling of the multi-objective routing optimization problem by comprehensively considering various routing performance indicators through a mixed-objective approach. Subsequently, based on the mathematical modeling of the routing problem, we employed the DDQN algorithm to improve the traditional route forwarding mechanism of GPSR and constructed a DDQN model tailored to solve the routing problem in this study. Finally, to evaluate the performance of the proposed algorithm, we integrated the NS-3 network simulator with an AI framework using the NS3-AI interface. This integration allowed us to implement the DDQN-MTGPSR intelligent routing protocol and verify its advantages through simulation experiments. The results demonstrate that DDQN-MTGPSR outperforms comparative routing protocols in large-scale and highly dynamic networks.  
Although this study has made some progress in optimizing routing protocols for UAV clusters, some important research issues still need to be further explored. Firstly, the broadcast beacons in DDQN-MTGPSR include additional fields beyond traditional GPSR location information, leading to increased control overhead compared to conventional GPSR routing protocols. Additionally, the exploration-exploitation balance mechanism employs a fixed exploration rate (\(\varepsilon\)), which may not be optimal for all network conditions.  
Future work will focus on introducing an adaptive HELLO message mechanism to reduce control overhead by minimizing the frequency of control information transmissions. To enhance algorithm performance, we plan to design an adaptive \(\varepsilon\) based on time or network stability, reducing unnecessary exploration under stable conditions to improve convergence and routing efficiency. In addition, as the scale of UAV clusters continues to expand and the application scenarios become more complex, dynamic management of nodes will become one of the key challenges. Future work can explore how to better  
Figure 10: Variation of PRE with the scale of the network.  
cope with network topology changes caused by nodes' addition or exit. Introducing more flexible and adaptive mechanisms can better support the practical application of UAV clusters in complex and dynamic environments.  
Conceptualization, H.C.; Methodology, H.C. and F.L.; Software, H.C., F.L. and J.Z.; Validation, F.L., J.Z. and Y.D.; Investigation, H.C. and Y.D.; Writing--original draft, Y.D.; Writing--review & editing, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.  
The authors declare no conflict of interest.