## 6 Simulation Results and Discussions  
Using the BBO, PSO, FA, HPSO, and mLebTLBO algorithms in MATLAB, simulations were conducted in a WSN field with an individual anchor node and 20 movable target nodes. These were performed on a MacBook Air with an i5 processor and 4 GB of RAM. The target nodes are placed at random throughout the \(15\times 15\) m\({}^{2}\) sensor area, with the anchor node anchored in the center of the sensing area. When the target node drops inside the span of the anchor node, RSS values, as well as two virtual anchor nodes, are taken into account in order to estimate the set of parameters (as at least three anchor nodes are necessary for 2D placement). LOS issues can also be reduced when virtual anchor nodes are used. To determine the efficacy of the mLebTLBO algorithm, numerous simulations must be run. Simulations were carried out on a \(15\times 15\) m\({}^{2}\) sensor area. Although more than six virtual anchors can be projected (Table 2), in our study, six virtual anchors are necessary to meet the requirements of identifying nodes in a two-dimensional environment by choosing the three closest anchors. The simulations were performed to check the effectiveness of the simulation parameters of mLebTLBO; the parameters that give best results are presented in Table 3. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Co-Ordinates** & **AN** & **VN1** & **VN2** & **VN3** & **VN4** & **VN5** & **VN6** \\ \hline \(X\) & 7.5 & 10.136 & 5.279 & 3.519 & 4.527 & 9.736 & 11.521 \\ \(Y\) & 7.5 & 11.454 & 11.929 & 6.941 & 3.434 & 2.949 & 5.619 \\ Anchor node (AN) and Virtual Anchor Node (VN). & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Anchor and Virtual Anchor Nodes Co-ordinates. Figure 8: Error estimation using mLebTLBO. Each algorithm's strategic settings, such as those for the BBO, PSO, FA, HPSO, and mLebTLBO algorithms, are listed in Table 4.  
To locate itself within the recommended structure, each movable target node will use the BBO, PSO, FA, HPSO, and mLebTLBO optimization algorithms. The parameters for mobile node localization are the PSO, HPSO, and mLebTLBO algorithms. The parameter values of the anchored anchor and virtual anchor are shown in Table 2. The existing distance matrix between the anchor, virtual anchor, and target nodes is shown in Table 3. In this study, the anchor node is employed first, and the mobility of all target nodes is considered in five diverse movements. The analysis is carried out in a mobility-based scenario using BBO, PSO, FA, HPSO, and mLebTLBO. A fitness function is defined as the mean of the localization error given in Equation (16). Target nodes are first distributed at random, whereas the anchor node is fixed and situated in the middle of the sensing area. Then, for target nodes, movement is applied, and six virtual anchors at a 60\({}^{\circ}\) projection with regard to the anchor node are evaluated. The solutions of localization utilizing numerous optimization techniques such as BBO, PSO, FA, HPSO, and mLebTLBO are shown in the Figures 9-13. \begin{table}
\begin{tabular}{l l} \hline \hline
**Algorithm** & **Parameters** \\ \hline PSO & \(N=20\); \(M=2\); \(G_{\max}=50\); \(c_{1}\), \(c_{2}=2\); \(w=0.729\) \\ HPSO & \(N=20\); \(M=2\); \(G_{\max}=50\); \(c_{1}\), \(c_{2}\), \(c_{3}=1.494\); \(\eta=0.1\); \(w=0.729\) \\ BBO & \(N=20\); \(M=2\); \(G_{\max}=50\); \(p_{m}=0.05\) \\ FA & \(N=20\); \(M=2\);\(G_{\max}=50\); \(\kappa=0.2\); \(\gamma=0.96\) \\ mLebTLBO & \(N=20\); \(M=2\); \(G_{\max}=50\); \(F=0.9\); \(L\)Emin = 0.3; \(CR=0.5\) \\ \hline \hline \end{tabular} Here, \(N\) refers to the size of population, \(M\) denotes number of dimensions of problem, \(G_{\max}\) is number of iterations.  
\end{table} 
Table 4: Meta-Heuristic Algorithms Parameters Selection.