## 6 Simulation and Discussion
The distance traveled by a MR during surveillance through defined surveillance locations is abbreviated in Table 3. The distance traveled by a MR from r\({}_{1}\) to r\({}_{2}\) is 4.12, the distance traveled by a MR from r\({}_{2}\) to r\({}_{3}\) is 3.16, the distance traveled by a MR from \(r_{3}\) to r\({}_{4}\) is 4.52, the distance traveled by a MR from r\({}_{4}\) to r\({}_{5}\) is 3.16, distance traveled by a MR from r\({}_{5}\) to r\({}_{6}\) is 5.50, and distance traveled by a MR from r\({}_{6}\) to r\({}_{1}\) is 5.59 units. Thus, the total distance traveled by a MR during the surveillance is 26.05 units. The required energy and time for a MR navigation from one coordinate to another coordinate in the surveillance zone and the distance traveled by a MR are illustrated in Table 4. The total energy used by a MR during the performance of the surveillance task is 56.05 units, and the total time spent by a MR during the surveillance task is 25.03 units. Thus, the remaining energy and time that can be estimated for the navigation of a MR at the shortest path determined by the PSO technique during surveillance will be 33.95 and 74.97 units, respectively. The enhanced intelligent PSO algorithm provides more efficient iterations, a reduced anticipated illustrates path, and optimized required energy and time with higher data acquisition for the surveillance, as demonstrated by a comparison with simulated analyses that illustrates the technique's performance. The benchmark functions from [45], including the Sphere function, Ackley function, Rosenbrock function, and Griewangk function, are used for the comparative analysis of the presented technique. A fitness function must be built in order to assess if the present route is superior to the prior one and whether it is the global preferred option while evaluating the path that PSO sought. Tracking the fitness value across PSO iterations is essential for interpreting the convergence behavior and algorithmic performance. The term \"fitness value over iterations\" describes how a PSO algorithm's level of accuracy in solutions evolves over time, whether through iterative refinement or generational growth. Figure 6 presents a comparative analysis between different benchmark functions for fitness value over the number of iterations. The fitness value versus iterations graph shows higher convergence with several iterations during simulation. A greater level of convergence is achieved, and localized capture events are less common during the simulation. This enhanced PSO algorithm includes local search techniques for refining responses within very small areas surrounding the present particle positions, which helps during the fine-tuning of responses and might result in a more efficient use of search space. Improved PSO algorithms use better starting techniques to properly disperse particles over the search space, preventing early convergence to poor responses. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Calculated Value** & **r\({}_{1}\)- r\({}_{2}\)** & **r\({}_{2}\)- r\({}_{3}\)** & **r\({}_{3}\)- r\({}_{4}\)** & **r\({}_{4}\)- r\({}_{5}\)** & **r\({}_{5}\)- r\({}_{6}\)** & **r\({}_{6}\)- r\({}_{1}\)** \\ \hline Distance & 4.12 & 3.16 & 4.52 & 3.16 & 5.50 & 5.59 \\ Energy & 9.12 & 8.16 & 9.52 & 8.16 & 10.50 & 10.59 \\ Time & 4.06 & 3.58 & 4.26 & 3.58 & 4.75 & 4.80 \\ \hline \end{tabular}
\end{table} 
Table 4: Illustration of total distance covered, total used energy, and total required time for the navigation of a MR between one surveillance location to another surveillance location. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Coordinates** & **Obs1** & **Obs2** & **Obs3** & **Obs4** & **Obs5** & **Obs6** & **Obs7** & **Obs8** & **Obs9** & **Obs10** & **Obs11** \\ \hline X & 1.5 & 1.5 & 3.0 & 2.5 & 5.0 & 6.0 & 5.25 & 4.0 & 9.0 & 8.5 & 8.25 \\ Y & 8.0 & 3.0 & 6.25 & 3.5 & 9.0 & 6.5 & 3.5 & 1.0 & 1.75 & 7.0 & 8.75 \\ Radius & 0.5 & 0.5 & 0.75 & 0.25 & 0.5 & 1.0 & 1.5 & 0.5 & 0.75 & 0.5 & 0.75 \\ \hline \end{tabular}
\end{table}