## 6 Simulation and Discussion
A real-world investigation of airborne micro-robotic swarms acts as the source of inspiration for this research's basic concept. The planned application of the proposed framework and its related characteristics is to give an effective algorithmic structure of both energy usage and surveillance within the flying swarm. Establishing a baseline for more feasible investigations is the objective of the experiments. The MR route is designed to prevent collisions with obstacles, which is accomplished by implementing a collision monitoring and avoidance algorithm. Figure 5 shows the complete surveillance zone, the green line shows the optimized shortest distance between two surveillance locations, and the red dotted line shows the normal shortest distance while avoiding obstacles. Figure 4: Flowchart of the proposed algorithm. We perform simulations to demonstrate the efficiency of our presented intelligent PSO approach while comparing it to various widely recognized PSOs. The following is a list of the parameter configurations. The \(x\)-range can be expressed as the difference between the particle's highest and lowest velocities, which are 10% of the search space's radius. The highest and lowest velocities of particles for the Sphere function (\(f_{s}\)) in this methodology is [10, \(-\)10]. The level-by-level competitive technique determines the learning and weight variables in intelligent PSO. The number of particles and iterations are 300 and 1000, respectively. The particle dimension is considered as 30, and each experiment is repeated 50 times. The median outcome is then given after the findings are evaluated individually to remove the random contribution. Table 2 describes the X and Y coordinates of different data acquisition locations inside the surveillance zone. We consider that the surveillance region is a type of rectangular area with 10 units in length and 10 units in width. The location of the start has been considered as being at the lower left corner. A total of six data acquisition locations have been generated randomly in the surveillance zone, and their coordinates are described in Table 1. Here, \(\mathbf{r}_{1}\) is the starting and final position for the surveillance zone. The required energy for MR for each data acquisition place is five units, \(E_{r}=5\). The used energy per unit length is 1 unit, \(E_{l}=1\). The total used time by MRs at every data acquisition place is 2, \(t_{r}=2\). The total distance traveled by MR per unit of time is 0.5, \(t_{n}=0.5\). Let us consider the energy and time left for the re-planning of surveillance tasks are 90 and 100 units, which means \((E_{initial}-E_{U})=90\), and \((t_{initial}-t_{U})=100\). There are a total of 11 obstacles available inside the surveillance zone, which are taken randomly with different shapes and locations. Table 3 describes the X and Y coordinates and the radius of obstacles available inside the surveillance zone. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Coordinates** & \(\mathbf{r_{1}}\) & \(\mathbf{r_{2}}\) & \(\mathbf{r_{3}}\) & \(\mathbf{r_{4}}\) & \(\mathbf{r_{5}}\) & \(\mathbf{r_{6}}\) \\ \hline X & 2.5 & 1.5 & 2.5 & 7 & 8 & 8 \\ Y & 1.5 & 5.5 & 8.5 & 8 & 5 & 2.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Illustration of data acquisition locations in the surveillance region. Figure 5: Illustration of the shortest path for performing surveillance tasks at different locations be listed as (**a**) shortest path between each surveillance location as a red dotted line without optimization and (**b**) optimized shortest path between each surveillance location shown as a green path and without optimized is the dotted path.