## 5 Simulation and Discussion
Compared with the EA, SA and SFLA, the NAEEA reduces the network energy consumption by 18.67%, 15.28% and 22.78%, respectively. Therefore, compared with the EA, SA and SFLA, the NAEEA can generate a better clustering scheme for AUSCNs. Table 11 shows the experimental parameters of Figure 13; the number of UAVs and monitoring range remain unchanged, while increasing the cluster head ratio of the network. According to the experimental parameters in Table 11, Figure 13 shows the relationship between the network energy consumption and the number of iterations for the NAEEA, EA, SA and SFLA, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **UAVs** & **Cluster Head Ratio** & **Monitoring Range** \\ \hline Figure 12a & 200 & 0.05 & 200 \(\times\) 200 m\({}^{2}\) \\ Figure 12b & 200 & 0.05 & 300 \(\times\) 300 m\({}^{2}\) \\ Figure 12c & 200 & 0.05 & 400 \(\times\) 400 m\({}^{2}\) \\ Figure 12d & 200 & 0.05 & 500 \(\times\) 500 m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The experimental conditions in Figure 12.  
Figure 12: Network energy consumption of four algorithms under different monitoring ranges. (a) 200 \(\times\) 200 m\({}^{2}\), (b) 300 \(\times\) 300 m\({}^{2}\), (c) 400 \(\times\) 400 m\({}^{2}\), (d) 500 \(\times\) 500 m\({}^{2}\). Figure 13a-d are graphs of the network energy consumption of the four clustering methods of the NAEEA, EA, SA and SFLA, respectively, at different cluster head ratios. 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. It can be seen from the simulation results that the SA keeps decreasing in the early stages of the algorithm iterations, but reaches evolutionary stagnation after the number of iterations reaches a certain number, and the final network energy consumption is high. The performance of the EA is relatively stable during the operation of the algorithm, but it is easy to fall into premature convergence during the evolution process, causing the algorithm to evolve slowly in the later stage and being unable to obtain the optimal clustering scheme. Due to the limitation of the fixed parameters during the evolution process, the SFLA results in a smaller population solution space, and the final network energy consumption is the highest. Due to the niche strategy adopted, which performs group evolution first and then fusion after population initialization, the NAEEA avoids the evolutionary stagnation that is caused by falling into local optimality. By conducting parallel searches in the solution space through adaptive strategies and elite strategies, and by dynamically adjusting the algorithm parameters during operation, the diversity of the population solutions is expanded and the convergence speed is accelerated. It can be seen from Tables 12 and 13 that the network energy consumption of the NAEEA is lower than that of the EA, SA and SFLA, and under the same number of UAVs and monitoring ranges and different cluster head ratios, the network energy consumption of the NAEEA is compared with the other algorithms. The AUSCN clustering scheme generated by the NAEEA has the highest energy utilization compared to the other algorithms, with an extension of 8.89% to 21.43%, an extension of 6.46% to 25.00%, and an extension of 15.75% to 25.00%.