## 6 Results and Discussion  
In this section, the AD-ADENSGA will be compared with ADENSGA with basic binary encoding (ADENSGA) [10] and decreasing any number encoded ADENSGA with a fixed change rule of any number (D-ADENSGA) mentioned in Section 4.1. Figure 8: The average any number changing with the generation for different values of \(k_{min}\). Figure 7: The optimal results for different values of \(k_{min}\) of node placement 4. According to the rough degressive rules of any number concluded in Section 4.3, a similar fitting function can be found as the degressive rule of D-ADENSGA, which is shown in Equation (12). \[N(k)=max[2,ceil(N(0)-\frac{(N(0)-2)\cdot 30^{7}}{30^{7}+k^{7}})] \tag{12}\]  
The experimental environment has been described in Section 4.2 and the parameters used in the experiment are shown in Table 1. In addition, set \(Score_{limit}=0.035\), \(k_{limit}=40\), \(k_{max}=200\). According to the parameter experiments in Section 5, set \(N(0)=50\), \(k_{min}=15\). The curves of any number changing with the generation are shown in Figure 9. The curves respectively represent the change of any number for AD-ADENSGA in five runs, ADENSGA and D-ADENSGA. Figure 10 shows the results of ADENSGA, D-ADENSGA and AD-ADENSGA in each generation. Among them, Figure 10a shows the average Coverage curves, Figure 10b shows the average Rest Energy curves, Figure 10c shows the average _Score_ curves and Figure 10d shows the max _Score_ curves. The average Coverage curves, average Rest Energy curves and average _Score_ curves respectively represent the average of the mean value of the first dominance level's Coverage, Rest Energy and _Score_ in five runs. The max _Score_ curves represent the average of the maximum value of the first dominance level's _Score_ in five runs. Figure 10a,b show that AD-ADENSGA and D-ADENSGA can achieve higher coverage in less time, while the optimization effect of AD-ADENSGA on energy consumption is better than D-ADENSGA and ADENSGA. Figure 10c,d show that the optimization effects of both AD-ADENSGA and D-ADENSGA are better than ADENSGA. To further analyze the optimization of these three algorithms, the results of the 200 th generation (\(k=200\)) are shown in Table 4. Gn95 means the generation in which Average Coverage reaches 0.95 for the first time, where the optimal results are highlighted in bold. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \begin{tabular}{c} **Average** \\ **Coverage** \\ \end{tabular}  & \begin{tabular}{c} **Average Rest** \\ **Energy/J** \\ \end{tabular}  & \begin{tabular}{c} **Average** \\ _Score_ \\ \end{tabular}  & \begin{tabular}{c} **Max** \\ _Score_ \\ \end{tabular}  & \ \begin{tabular}{c} **Average** \\ _D/m_ \\ \end{tabular}  & \begin{tabular}{c} **Gn95/** \\ _Generations_ \\ \end{tabular}  \\ \hline Initial Node Placement & 0.7919 & 0.2749 & 0.2177 & 0.2177 & - & - \\ \hline ADENSGA & 0.9392 & 0.2851 & 0.2676 & 0.2776 & 421.7 & \textgreater{}200 \\ \hline D-ADENSGA & **0.9443** & 0.2841 & 0.2682 & 0.2751 & 414.4 & \textgreater{}20  
Table 4 shows that the coverage and rest energy of D-ADENSGA and AD-ADENSGA are all much better than ADENSGA with less moving distance \(D\). The optimization effect of AD-ADENSGA is better than D-ADENSGA and ADENSGA. In addition, AD-ADENSGA can obtain the required coverage faster than D-ADENSGA and ADENSGA. In order to eliminate the contingency and ensure the optimization effect for different initial node placements, two other random initial node placements are tested in this paper. The results of node placement 6 are shown in Figure 11 and Table 5.  
Figure 10(a),b show that AD-ADENSGA can still achieve a certain coverage in less time, and it can also achieve a best optimization effect on energy consumption. However, the optimization effect and speed of D-ADENSGA are about the same as ADENSGA.