## 5 Simulation and Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter** & **Value** \\ \hline \multirow{6}{*}{NAEEA} & Mutation probability & 0.05 \\  & Crossover probability & 0.8 \\  & Population size & 40 \\  & Number of iterations & 100 \\ \hline \multirow{6}{*}{EA} & Mutation probability & 0.05 \\  & Crossover probability & 0.8 \\  & Population size & 40 \\  & Number of iterations & 100 \\ \hline \multirow{6}{*}{SA} & Initial temperature & 1000 \({}^{\circ}\)C \\  & Temperature coefficient & 0.9 \\ \cline{1-1}  & Markov chain length & 100 \\ \cline{1-1}  & The number of iterations at the same temperature & 40 \\ \hline \multirow{6}{*}{SFLA} & Number of frogs & 40 \\ \cline{1-1}  & Number of groups & 4 \\ \cline{1-1}  & Number of iterations & 100 \\ \cline{1-1} \cline{2-2}  & Number of iterations in the group & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Detailed parameters of the algorithm. Figure 10: Comparison of energy consumption of four algorithms for UAVs of different sizes. (a) 100 UAVs, (b) 200 UAVs, (c) 300 UAVs, (d) 400 UAVs. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **UAVs** & **Cluster Head Ratio** & **Monitoring Range** \\ \hline Figure 10a & 100 & 0.05 & 300 \(\times\) 300 m\({}^{2}\) \\ Figure 10b & 200 & 0.05 & 300 \(\times\) 300 m\({}^{2}\) \\ Figure 10c & 300 & 0.05 & 300 \(\times\) 300 m\({}^{2}\) \\ Figure 10d & 400 & 0.05 & 300 \(\times\) 300 m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The experimental conditions in Figure 10.  
Table 8 shows the network energy consumption of the four algorithms in Figure 10 for drones of different sizes. It can be seen that the network energy consumption of the NAEEA in four different scenarios is lower than that of the EA, SA and SFLA. Moreover, as the scale of the drones increases, the network energy consumption of the four algorithms decreases. Table 9 shows the percentage of the network energy consumption that is reduced through the NAEEA compared with the EA, SA and SFLA in the four scenarios in Figure 10. It can be seen that the optimization effect of the NAEEA is very stable, and compared with the EA, SA and SFLA, the maximum optimization percentages are 11.43%, 12.85% and 23.86%, respectively. Figure 10a-d are graphs of the network energy consumption of the four clustering methods of the NAEEA, EA, SA and SFLA, respectively, in UAVs of different sizes. It can be clearly seen that after 100 iterations, the network energy consumption of the NAEEA is lower than that of the EA, SA and SFLA. In Figure 10a, the SA and EA have a certain decline in the early stages of the algorithm iterations, but they fall into evolutionary stagnation after the number of iterations reaches 25 generations, and the network energy consumption of the finally obtained clustering scheme is relatively large. The SFLA performed poorly during operation. Due to the inability to dynamically adjust the algorithm parameters, the algorithm evolved slowly. The evolution stagnated after 71 generations, and the final convergence result was higher than the other three algorithms. After the NAEEA is initialized, the population is grouped and evolved through the niche strategy, which can well expand the population search space, and the elite strategy adopted can well inherit the best individuals in the population into the next iteration, speeding up the algorithm convergence speed, so better results are obtained in the early stage of the algorithm. At the same time, because the NAEEA adopts an adaptive strategy, the algorithm can dynamically adjust the mutation probability during operation to avoid evolutionary stagnation and premature convergence that are caused by the fixed parameters of the algorithm. As can be seen from Table 8, the final convergence network energy consumption of the four clustering methods of the NAEEA, EA, SA and SFLA are 2.17 J, 2.40 J, 2.49 J and 2.85 J, respectively.