# Multi-Objective Optimized GPSR Intelligent Routing Protocol for UAV Clusters  
[PERSON]  
1 Wuhan Maritime Communication Research Institute, Wuhan 430010, China; [EMAIL_ADDRESS] (H.C.); [EMAIL_ADDRESS] (F.L.)1 Wuhan Maritime Communication Research Institute, Wuhan 430010, China; ch83825@msn.  
UAV clusters move unpredictably and randomly for extended periods, making it crucial to develop intelligent routing protocols that quickly adapt to network changes. Effective routing decision-making is vital for improving data delivery rates and reducing network delay, optimizing overall performance.  
Traditional routing protocols are categorized into topology-based and geolocation-based protocols. Topology-based protocols include (1) proactive routing protocols: examples are Destination-Sequenced Distance-Vector (DSDV) [14] and Optimized Link State Routing (OLSR) [15], which are slow to react to dynamic topologies, leading to delays, routing loops, and blind routes [16]. (2) Reactive Routing Protocols: examples are Dynamic Source Routing (DSR) [17] and Ad Hoc on-Demand Distance Vector Routing (AODV) [18]. In large UAV networks, these may face routing failures, increased delay, and high bandwidth consumption. (3) Hybrid Routing Protocol (HRP): this combines proactive and reactive approaches, maintaining different routing tables for area and out-of-area members [19]. However, they face higher computational complexity and overhead due to complex clustering processes [20].  
Geolocation-based routing protocols differ from topology-based protocols by not relying on complex routing tables but using the location information of destination nodes and neighbors for path selection. This makes them more suitable for highly dynamic UAV networks. A typical geolocation routing protocol is Greedy Perimeter Stateless Routing (GPSR) [21], which relies on instantaneous location information for better performance in dynamic environments. However, GPSR is sensitive to node density, and GPSR only focuses on distance information when forwarding without considering factors such as node load, which may lead to problems such as link congestion, thus reducing network performance.  
Recently, optimizing traditional routing protocols with machine learning algorithms has become a major research focus. These protocols adaptively learn the environment state, predict topological changes, and improve communication quality and energy efficiency. The main categories are reinforcement learning (RL)-based, deep learning (DL)-based, and deep reinforcement learning-based (DRL).  
The main contributions in this study are as follows:  
* Mathematical modeling of multi-objective routing optimization problem: combines the multi-objective optimization mechanism of DDQN, transforming the route forwarding process into a Markov decision process (MDP) and modeling the multi-objective routing optimization problem by comprehensively considering multiple routing performance metrics in a mixed-objective way.
* DDQN-based GPSR optimization: uses DDQN to improve the traditional GPSR routing mechanism, constructing a DDQN network model to solve the routing problem.
* NS-3-based implementation and validation: combines the NS-3 network simulator with an AI framework via the NS3-AI interface to integrate and validate the DDQN-MTGPSR intelligent routing protocol, showing superior performance in large-scale, highly dynamic networks compared to other routing protocols.  
The rest of the paper is structured as follows. Section 2 reviews related research on UAV cluster routing protocols. Section 3 presents the mathematical modeling of the routing optimization problem. Section 4 describes the design process of the DDQN-MTGPSR protocol. Section 5 covers the simulation experiments and analysis. Section 6 summarizes the research and suggests directions for future improvement.