## 6 Simulation Results
### Algorithm Comparison Experiment
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **MOEA/D-** \\ **MFAR** \\ \end{tabular} & \begin{tabular}{c} **MOEA/D-** \\ **MFAR-ndr** \\ \end{tabular} & **MOEA/D-** & **NSGA-II** & **MOPSO** & \
\begin{tabular}{c} **MOEA/D-** \\ **MFAR** \\ \end{tabular} &
\begin{tabular}{c} **MOEA/D-** \\ **MFAR-ndr** \\ \end{tabular} & **MOEA/D** & **NSGA-II** & **MOPSO** \\ \hline Scenario & \multicolumn{6}{c}{Scenario 2} \\ \cline{2-10} HV & \(\mathbf{5.49\times 10^{11}}\) & \(5.19\times 10^{11}\) & \(4.64\times 10^{11}\) & \(3.39\times 10^{11}\) & \(2.39\times 10^{11}\) & \(\mathbf{7.57\times 10^{11}}\) & \(6.71\times 10^{11}\) & \(6.19\times 10^{11}\) & \(4.61\times 10^{11}\) & \(2.15\times 10^{11}\) \\ Number of solution & **4290** & 113 & 197 & 165 & 26 & \(\mathbf{977}\) & 102 & 134 & 64 & 39 \\ \hline Scenario & \multicolumn{6}{c}{Scenario 3} \\ \cline{2-10} HV & \(\mathbf{9.06\times 10^{11}}\) & \(8.4\times 10^{11}\) & \(8.3\times 10^{11}\) & \(5.82\times 10^{11}\) & \(3.62\times 10^{11}\) & \(\mathbf{1.33\times 10^{12}}\) & \(1.13\times 10^{12}\) & \(1.13\times 10^{12}\) & \(8.76\times 10^{11}\) & \(4.95\times 10^{11}\) \\ Number of solution & **4620** & 106 & 252 & 97 & 44 & \(\mathbf{346}\) & 80 & 166 & 77 & 24 \\ \hline Scenario & \multicolumn{6}{c}{Scenario 5} \\ \cline{2-10} HV & \(\mathbf{1.75\times 10^{17}}\) & \(1.13\times 10^{17}\) & \(8.36\times 10^{14}\) & \(4.54\times 10^{18}\) & \(1.72\times 10^{16}\) & \(\mathbf{2.61\times 10^{17}}\) & \(1.77\times 10^{12}\) & \(1.28\times 10^{17}\) & \(7.57\times 10^{18}\) & \(2.43\times 10^{16}\) \\ Number of solution & **3639** & 383 & 679 & 642 & 113 & \(\mathbf{3905}\) & 380 & 729 & 661 & 83 \\ \hline Scenario & \multicolumn{6}{c}{Scenario 7} \\ \cline{2-10} HV & \(\mathbf{3.6\times 10^{17}}\) & \(2.42\times 10^{17}\) & \(1.95\times 10^{17}\) & \(9.76\times 10^{18}\) & \(3.86\times 10^{16}\) & \(\mathbf{5.45\times 10^{17}}\) & \(3.62\times 10^{17}\) & \(3.21\times 10^{17}\) & \(1.37\times 10^{17}\) & \(8.98\times 10^{16}\) \\ Number of solution & **3612** & 370 & 888 & 509 & 56 & \(\mathbf{2731}\) & 431 & 747 & 584 & 23 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: HV and number of feasible solutions. Figure 9: PFs of MOEA/D-MFAR, MOEA/D, NSGA-II, and MOPSO in eight scenarios. (**a**) Scenario 1. (**b**) Scenario 2. (**c**) Scenario 3. (**d**) Scenario 4. (**e**) Scenario 5. (**f**) Scenario 6. (**g**) Scenario 7. (**h**) Scenario 8. Moreover, to monitor the convergence process of different algorithms, the HV values for each epoch of each algorithm in various scenarios are recorded and depicted in Figure 10. Across all scenarios, the HV curves corresponding to MOEA/D-MFAR consistently reach the highest values, followed by MOEA/D, NSGA-II, and MOPSO. Each HV curve corresponding to MOEA/D exhibits a continuous upward trend, indicating that it has not yet converged throughout the optimization process. Conversely, the HV curves for NSGA-II and MOPSO display initial upward trends in the early epochs, but often experience slow or plateaued growth thereafter. This suggests that benchmark algorithms lack robust solution search capabilities for this type of problem, making them susceptible to local optima and resulting in slow convergence. Additionally, it is noteworthy that problems with varying numbers of optimization objectives exhibit different complexities. For the curves corresponding to MOEA/D-MFAR, it is apparent that convergence speed in the three-objective optimization problem (Figure 9e-h) is relatively slower than in the two-objective optimization problem (Figure 9a-d). In the multi-UAV scenario, convergence speed (Figure 9a,c,e,g) is slower compared to the single-UAV scenarios (Figure 9b,d,f,h). Similarly, to assess the fairness of resource allocation among IoT devices of the same type, the ASDR values corresponding to the four algorithms are calculated and presented in Table 6. The best values are bolded.