## 6 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Average** & **Average Rest** & **Average** & **Max** & **Average** & **Gn95/** \\  & **Coverage** & **Energy/J** & _Score_ & _Score_ & _D/m_ & _Generations_ \\ \hline Initial Node Placement & 0.8849 & 0.2758 & 0.2440 & 0.2440 & - & - \\ \hline ADENSGA & 0.9780 & 0.2907 & 0.2841 & **0.2931** & **397.8** & 41 \\ \hline D-ADENSGA & **0.9783** & 0.2905 & 0.2840 & 0.2913 & 403.4 & 34 \\ \hline AD-ADENSGA & 0.9741 & **0.2927** & **0.2850** & 0.2913 & 403.4 & **26** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results for Node Placement 6. Figure 10: The optimal results of ADENSGA, D-ADENSGA and AD-ADENSGA for node placement 5. Figure 11c,d show that the optimization effect of AD-ADENSGA is better than D-ADENSGA and ADENSGA. Table 5 shows that the coverage and the rest energy of AD-ADENSGA are much better than D-ADENSGA and ADENSGA. The optimization effect of D-ADENSGA is similar to ADENSGA, but with a longer moving distance \(D\). In addition, AD-ADENSGA can obtain the required coverage faster than D-ADENSGA and ADENSGA. The results of node placement 7 are shown in Figure 12 and Table 6.  
Figure 12a,b show that D-ADENSGA can achieve higher coverage in the least time, while AD-ADENSGA can achieve the best optimization effect on energy consumption. AD-ADENSGA can also achieve higher coverage in less time, but the speed is slower than D-ADENSGA. Figure 12c,d show that the optimization effect of D-ADENSGA is better than AD-ADENSGA, and the optimization effect of AD-ADENSGA is better than ADENSGA. \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 & 0.8697 & 0.2736 & 0.2379 & 0.2379 & - & - \\ \hline ADENSGA & 0.9741 & 0.2870 & 0.2792 & 0.2894 & 408.6 & 52 \\ \hline D-ADENSGA & **0.9850** & 0.2902 & **0.2858** & **0.2919** & 411.8 & **21** \\ \hline AD-ADENSGA & 0.9773 & **0.2911** & 0.2844 & 0.2911 & **396.1** & 33 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results for Node Placement 7. Figure 11: The optimal results of ADENSGA, D-ADENSGA and AD-ADENSGA for node placement 6. Table 6 shows that the coverage and rest energy of both AD-ADENSGA and D-ADENSGA are much better than ADENSGA with less moving distance \(D\). The optimization effect of D-ADENSGA is the best, but AD-ADENSGA can achieve a similar optimization effect with a shorter moving distance \(D\). In addition, D-ADENSGA can obtain the required coverage faster than AD-ADENSGA and ADENSGA. It can be concluded that both AD-ADENSGA and D-ADENSGA can achieve better optimization results than ADENSGA, among which, AD-ADENSGA has the best optimization effect, speed and stability. In other words, applying degressive encoding to a multi-objective optimization algorithm is beneficial to solve the node placement problem of WSN. Due to the fixed rule of degressive any number encoding, D-ADENSGA can only achieve better optimization results in some initial node placements. The adaptive change rule of any number proposed in this paper is feasible. It can improve the optimization performance and stability of the algorithm.