## 5 Simulation Results and Discussion  
In this paper, we developed a Matlab-based operational environment to evaluate the working performance of the proposed multi-UAV path planning system. The main simulation parameters are listed in Table 2.  
Figure 2: Path Planning Flowchart. For PSO algorithm parameter setting, the authors in [41] suggested the value of important parameters, like inertia, is 0.7298 and both the cognitive value as 1.4960. The authors in [42] suggested that inertia value can be selected within the value range from 0.4 to 0.9 and optimal value can be achieved by trial and error methodology, where the cognitive value can be selected as 2.0. We conducted a simulation by using both values and found that the second parameter value set has the better result, in terms of our simulation parameters. Therefore, the resulting values of the parameter have been used in this paper. We demonstrate the comparison between conventional PSO and PSO with modified parameters (mPSO) in Figure 3a. The result shows that the fitness value of the proposed modified-PSO (mPSO) algorithm converges faster to a stable value as the number of iterations increases. In the simulation, mPSO achieved higher Fitness values in a much shorter period of time than the conventional PSO, which led UAVs to surveillance full areas in a short period of time. As the fitness value is the major factor for our proposed trajectory planner, we compared PSO and mPSO for their fitness value, in terms of the convergence over the number of iterations. This is the reason why we followed mPSO for further experimentation. These parameter values of PSO and mPSO are given in Table 3.  
As we increased the number of particles, we observed an improvement in the dynamic fitness function. The observed improvement of the dynamic fitness function is shown in Figure 3b. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Symbols** & **Values** \\ \hline Grid Side (2-D environment) & - & \(20\times 20\) \\ \hline Operational Space (3-D environment) & - & \(20\times 20\times 1.5\) \\ \hline Number of drones & \(N_{D}\) & 4 \\ \hline Drone unit power & \(P_{w}\) & 20 \\ \hline Monitoring drone speed & \(v\) & 10 m/s \\ \hline Number of particles & \(N_{part}\) & 32, 64, 128, 150, 256, 512 \\ \hline Number of iterations & \(N_{iter}\) & 100 \\ \hline Minimum safe distance & \(d_{min}\) & 0.2 m \\ \hline Initial SAI value & \(v_{cellx}^{ini}\) & 4 to 10 \\ \hline Initial environment risk & \(r^{e}\) & 1–5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. Figure 3: Fitness value, (**a**) comparison between Conventional PSO versus modified PSO, and (**b**) effect of the number of particles on dynamic fitness value. In the objective function designing, we had to consider the impact of energy consumption, flight risk estimation, and SAI value on each other, where all the values had been normalized. It was supposed to have the optimal flight path, which consists of less energy consumption and flight risk, and a higher SAI value. For this reason, we designed the function by considering energy consumption and flight risk with a minimum value, where the SAI value has to be the maximum value. Figure 4 represents the objective function value curve and total fitness values under EC, FRE, SAI, respectively, where the number of iterations is 100 and the number of particles is 150. It shows the trajectory optimization performance of the flight time, in terms of energy consumption, fight risk, and surveillance area importance. As the number of iterations increased, the energy consumption and flight risk minimized and maintained a stable value, while their surveillance area importance values maximized. From the simulation results shown in Figure 4, it is observed that the simulation begins with the last value achieved from the dynamic fitness function. As the number of iterations increased the energy consumption, flight risk, and SAI value converged quickly and gradually improved the performance of the dynamic fitness function.