## 5 Simulation and Discussion
As can be seen from Table 8, after the convergence of the four algorithms is terminated, compared with the GA, ACO and PSO, the EACACA reduces network energy consumption by 9.58%, 12.85% and 23.86%, respectively. In Figure 10b, it can be seen that the NAEEA obtained better values than the EA, SA and SFLA in the early iteration, and obtained a more stable clustering scheme in the 12 th generation. This is because the NAEEA adopts a niche strategy, adaptive strategy and elite strategy, which can expand the diversity of the solutions of the population in the early stage of population evolution, further evolve the genes of local optimal values, speed up the convergence speed of the algorithm and avoid falling into local optimal solutions. On \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **100 UAVs (J)** & **200 UAVs (J)** & **300 UAVs (J)** & **400 UAVs (J)** \\ \hline NAEEA & 2.17 & 1.98 & 1.79 & 1.55 \\ EA & 2.40 & 2.20 & 2.00 & 1.75 \\ SA & 2.49 & 2.22 & 1.99 & 1.72 \\ SFLA & 2.85 & 2.47 & 2.19 & 1.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Network energy consumption generated with different numbers of UAVs. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **EA** & **SA** & **SFLA** \\ \hline Figure 10a & 9.58\% & 12.85\% & 23.86\% \\ Figure 10b & 10.00\% & 10.81\% & 19.84\% \\ Figure 10c & 10.50\% & 10.05\% & 18.26\% \\ Figure 10d & 11.43\% & 9.88\% & 19.69\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Compared with the EA, SA and SFLA, the NAEEA reduces the percentage of network energy consumption in Figure 10.  
the contrary, due to the limitations of evolutionary strategies and fixed parameters, the EA, SA and SFLA converge slowly and easily fall into local optimal solutions. Finally, when the 100-generation iteration stopped, the final convergence network energy consumption of the four clustering methods of the NAEEA, EA, SA and SFLA were 1.98 J, 2.20 J, 2.22 J, and 2.47 J, respectively. Compared with the GA, ACO and PSO, the EACACA reduces network energy consumption by 10.00%, 10.81% and 19.84%, respectively. Similarly, the running conditions of Figure 10c and d are the same as Figure 10b. After the iteration stops, the network energy consumption of the NAEEA is lower than that of the EA, SA and SFLA, and better clustering can be obtained. The specific clustering scheme is shown in Figure 11.  
Figure 11 shows the AUSCN clustering diagram based on the NAEEA under different-scale UAVs within a 300 \(\times\) 300 m\({}^{2}\) monitoring range, with a cluster head ratio of 0.05. From Figure 11a, it can be seen that it is divided into five cluster groups, in which the white circles are the member nodes in the cluster and the middle is the cluster head node. And in Figure 11, as the scale of the UAVs increases, the number of cluster heads also increases, and the distribution of the UAVs becomes denser. Table 10 shows the experimental parameters of Figure 12, increasing the monitoring range while fixing the number of UAVs and the cluster head ratio. According to the experimental parameters in Table 10, Figure 12 uses a bar chart to show the network energy consumption of the NAEEA, EA, SA and SFLA, respectively. Figure 11: AUSCN clustering diagram based on the NAEEA under different scales of UAVs. (**a**) 100 UAVs, (**b**) 200 UAVs, (**c**) 300 UAVs, (**d**) 400 UAVs. In Figure 12, the network energy consumption of the four algorithms in the monitoring range of 200 \(\times\) 200 m\({}^{2}\), 300 \(\times\) 300 m\({}^{2}\), 400 \(\times\) 400 m\({}^{2}\) and 500 \(\times\) 500 m\({}^{2}\) is more intuitively displayed in the form of a bar chart. It can be seen that the NAEEA is used in the four scenarios. The network energy consumption under the NAEEA is lower than that of the EA, SA and SFLA, and as the monitoring range increases, the network energy consumption of the four algorithms increases. In Figure 10a, the network energy consumption of the NAEEA, EA, SA and SFLA is 0.61 J, 0.75 J, 0.72 J and 0.79 J, respectively.