## 5 Results and Discussions
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Network Parameter**} & \multicolumn{3}{c}{**Values**} \\ \cline{2-4}  & **Case 1** & **Case 2** & **Case 3** \\ \hline Network terrain dimensions \((W\times L)\) & 50 m\({}^{2}\) & 100 m\({}^{2}\) & 200 m\({}^{2}\) \\ \hline Network size (in terms of number of sensor nodes, N) & 20 & 40 & 80 \\ \hline Sensing radius & 6 m & 8 m & 10 m \\ \hline Communication radius & 12 m & 16 m & 20 m \\ \hline Carrier frequency & 39 GHz & 39 GHz & 39 GHz \\ \hline Number of iterations & 100 & 200 & 300 \\ \hline \multicolumn{4}{c}{**Parameters of the various algorithms and their initialization**} \\ \hline CSO [23] & \(P_{a}=0.25\), \(\alpha_{0}=0.01\) & \\ \hline CD-CSO [9] & \(P_{a}=0.25\), \(P_{b}=0.35\)\(\alpha_{0}=0.01\) & \\ \hline ACSO [15] & \(P_{a}\)\({}_{MAX}=0.25\), \(P_{a}\)\({}_{MIN}=0.15\)\(\alpha_{0}=0.01\) & \\ \hline PSO-CSO [10] & \(P_{a}\)\({}_{MAX}=1\), \(P_{a}\)\({}_{MIN}=0.3\)\(\alpha_{0}=0.01\) & \\ \hline ICS-PSO-OBL [11] & \(P_{a}\)\({}_{max}=0.25\), \(V_{min}=-1\), \(V_{max}=1\),\(\alpha_{0}=1.3\) & \\ \hline ECSO-OBL [proposed] & \(P_{a}\)\({}_{MAX}=0.25\), \(P_{a}\)\({}_{MIN}=0.1\)\(\alpha_{0}=0.01\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters and values of the proposed algorithm. dimensions, and they take a greater number of iterations to achieve similar convergence accuracy, which indicates ineffective optimization. Therefore, the dimension-by-dimension updating mechanism in ECSO-OBL allows for the efficient updating of current optimal solution at each iteration. The integration of the ECSO-OBL algorithm with PSO significantly enhances the speed of convergence towards global optimal solutions. Also, the iterative tendency of the ECSO-OBL algorithm is better than the existing nascent coverage techniques and provides higher accuracy in the search for the global optimal solution. \begin{table}
\begin{tabular}{c c} \hline \hline
**Function** & **Definition** & **Interval** \\ \hline Beale & \(\left(1.5-x_{1}+x_{1}x_{2}\right)^{2}+\left(2.25-x_{1}+x_{1}x_{2}\right)^{2}+ \left(2.625-x_{1}+x_{1}x_{2}\right)^{2}\) & \(\left[-4.5\,4.5\right]\) \\ [PERSON] & \(\sum\limits_{i=1}^{D}\left[100\left(x_{i+1}-x_{i}^{2}\right)^{2}+\left(x_{i}- 1\right)^{2}\right]\) & \(\left[-30\,30\right]\) \\ Step & \(\sum\limits_{i=1}^{D}\left(x_{i}+0.5\right)^{2}\) & \(\left[-100\,100\right]\) \\ Sphere & \(\sum\limits_{i=1}^{D}x_{i}^{2}\) & \(\left[-5.12\,5.12\right]\) \\ Rastrigin & \(10+\sum\limits_{i=1}^{D}\left[x_{i}^{2}-10\cos(2\pi x_{i})\right]\) & \(\left[-5.12\,5.12\right]\) \\ Schwefel & \(\sum\limits_{i=1}^{D}\left[x_{i}\right]+\sum\limits_{i=1}^{D}\left[x_{i}\right]\) & \(\left[-100\,100\right]\) \\ Griewank & \(\sum\limits_{i=1}^{D}\dfrac{x_{i}^{2}}{4000}-\prod\limits_{i=1}^{D}cos\left( \dfrac{x_{i}}{\sqrt{f}}\right)+1\) & \(\left[-600\,600\right]\) \\ Schaffer & \(0.5+\dfrac{sin^{2}\left(x_{i}^{2}-12\right)-0.5}{\left[1+0.001\left(x_{i}^{2}+ x_{i}^{2}\right)\right]^{2}}\) & \(\left[-100\,100\right]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Benchmark functions for the current experiment. Figure 4: _Cont._  
Table 5 presents the performance results of the ECSO-OBL algorithm in comparison with the other nascent coverage algorithms in terms of iterations for convergence, coverage rate, and convergence speed. The simulations were conducted for different network sizes (in terms of number of sensor nodes) and dimensions (or for different network areas). For example, in a WSN of a size of 20 sensor nodes over an area of 50 m\({}^{2}\), shown in case 1, the coverage rate is improved by 15.58%, 14.16%, 23.41%, and 16.52% compared with the ICS-PSO-OBL, CD-CSO, ACSO, and PSO-CSO algorithms, respectively. Similarly, in case 2, we consider 40 sensor nodes over a 100 m\({}^{2}\) area, and the average and maximum coverage rates are presented in Figures 6 and 7. From these results, it is proved that the coverage rate is enhanced by 16.66%, 7.78%, 22.48%, and 11.34% by the ECSO-OBL algorithm.