## 4 Experimental Evaluation
### Comparison between Different Sampling Parameters  
In order to analyze the influence of sensor sampling parameters on optimization results, this subsection uses different sampling parameters to compare the average running speed and the average coverage rate of the space. Concretely, we discuss the influence of sampling parameters \(\Delta_{b},s_{\varphi}\) on optimization results.  
Qualitatively speaking, sensor position sampling interval \(\Delta_{b}\) determines the discrete values of sensor coordinates that can take in the optimization. The smaller the \(\Delta_{b}\), the more discrete the coordinate values that can be taken, and, accordingly, the larger the solution space to be searched. Similarly, the sampling number of sensor azimuth angle \(s_{\varphi}\) determines the discrete value that the sensor azimuth angle can take in the layout. The larger the \(s_{\varphi}\), the more discrete values the azimuth can take, and accordingly the larger the solution space the algorithm needs to search. To simplify the optimization process, we choose the GA to be the algorithm and 1-coverage as the objective. But the conclusions could also be applied to more complicated situations.  
As shown in Figure 7, as the sampling interval of the sensor position increases, there is a general downward trend in average coverage due to degradation in model accuracy. It is worth noting that the change in running time is not so noticeable. The possible reason is that the sampling interval increases, the solution space that needs to be searched becomes smaller, and then the search process seems to become faster. However, when there is more than one optimal placement scheme, but with fewer search options available, some solutions are missed, which reduces the number of optimal options available. Therefore, the difficulty in searching increases, and the search time is not significantly reduced. Figure 7 shows the results of different sampling numbers of the sensor azimuth angle. It is found that, as the number of sensor azimuth sampling points increases, the average running time increases, but the coverage does not increase steadily. The possible reason for this situation is that the experiment uses a relatively simple rectangular unobstructed target space. The four optional azimuths can already satisfy the necessary coverage requirements, so a slight increase in sampling points cannot significantly improve optimization performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Average Running Time (s)**} & \multicolumn{4}{c}{**Average Coverage**} & \multicolumn{4}{c}{**Standard Deviation of Coverage**} \\ \hline  & **PSO** & **GA** & **Combined** & **PSO** & **GA** & **Combined** & **PSO** & **GA** & **Combined** \\ \hline
1 & 20.82 & 148.607 & 85.453 & 0.820 & 0.840 & 0.846 & 0.017 & 0.008 & 0.002 \\ \hline
2 & 19.204 & 106.570 & 47.483 & 0.846 & 0.850 & 0.851 & 0.004 & 0.002 & 0.000 \\ \hline
3 & 18.347 & 173.23 & 132.873 & 0.726 & 0.745 & 0.753 & 0.033 & 0.023 & 0.003 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results for comparison of the different approaches.  
Figure 7: _Cont._