# Multi-Objective Optimized GPSR Intelligent Routing Protocol for UAV Clusters
## 3 Mathematical Modeling of Multi-Objective Routing Optimization Problems  
Traditional multi-objective routing optimization problems are often solved by metaheuristic algorithms such as Simulated Annealing (SA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). However, these algorithms do not guarantee finding an approximate optimal solution and face issues like delayed convergence and high computational complexity [29], especially in large-scale, highly dynamic, and complex network environments [30].  
In recent years, the DRL algorithm has demonstrated excellent performance in solving multi-objective optimization problems, particularly for large-scale and highly dynamic UAV cluster networks, DRL has emerged as an effective solution for multi-objective routing optimization, capable of adapting to complex and changing environments [31].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Routing Protocol** & **Protocol Type** & \begin{tabular}{c} **Geographical** \\ **Position** \\ \end{tabular} & \begin{tabular}{c} **Energy** \\ **Consumption** \\ **Factor** \\ \end{tabular} & \begin{tabular}{c} **Routing Hole** \\ **Factor** \\ \end{tabular} & \
\begin{tabular}{c} **Transmission** \\ **Speed/Delay** \\ **Factor** \\ \end{tabular} &
\begin{tabular}{c} **Relative** \\ **Moving Trend** \\ \end{tabular} \\ \hline Q-Routing [22] & RL & \(\checkmark\) & \(\mathsf{x}\) & \(\mathsf{x}\) & \(\mathsf{x}\) & \(\mathsf{x}\) \\ \hline QGEO [23] & RL & \(\checkmark\) & \(\mathsf{x}\) & \(\mathsf{x}\) & \(\checkmark\) & \(\mathsf{x}\) \\ \hline RFLQGEO [24] & RL & \(\checkmark\) & \(\mathsf{x}\) & \(\mathsf{x}\) & \(\checkmark\) & \(\checkmark\) \\ \hline GLAN [25] & RL & \(\checkmark\) & \(\mathsf{x}\) & \(\mathsf{x}\) & \(\mathsf{x}\) & \(\mathsf{x}\) \\ \hline DL-Aided Routing [27] & DL & \(\checkmark\) & \(\mathsf{x}\) & \(\mathsf{x}\) & \(\checkmark\) & \(\mathsf{x}\) \\ \hline NF-Routing [28] & DL & \(\checkmark\) & \(\mathsf{x}\) & \(\mathsf{x}\) & \(\checkmark\) & \(\checkmark\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of consideration factors for different routing protocols.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Routing Protocol** & **Application Scenario** & **Application Advantage** & **Application Disadvantage** \\ \hline RL-Routing & [22; 23; 24; 25] & & \begin{tabular}{c} Abstract formulation design, \\ Strong versatility and \\ adaptability, \\ Applications on dynamic or \\ unknown networks. \\ \end{tabular} & \begin{tabular}{c} Not applicable to \\ large-scale networks, \\ Application of multiple \\ objectives in routing problem. \\ \end{tabular} \\ \hline DL-Routing & [27; 28] & & \
\begin{tabular}{c} Explore the relationship \\ between environmental \\ characteristics and \\ optimal paths. \\ \end{tabular} &
\begin{tabular}{c} Network architecture design \\ training datasets, \\ Overfitting issues. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: A summary of the advantages and disadvantages of the application of RL and DL concepts in large-scale and highly dynamic unmanned clusters.