# Research on Transmission Task Static Allocation Based on Intelligence Algorithm
## 4 Experiments
### Algorithm Experiments  
Experiment 1: The objective is to verify the calculation accuracy of the algorithm through comprehensive evaluation value calculations. The parameter groups of all algorithms are calculated according to the iteration number of Formula (25), and the evaluation value is shown in Table 6.  
### Algorithm Experiments  
Experiment 1: The objective is to verify the calculation accuracy of the algorithm through comprehensive evaluation value calculations. The parameter groups of all algorithms are calculated according to the iteration number of Formula (25), and the evaluation value is shown in Table 6. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm and Parameter**} & \multicolumn{6}{c}{**Number of Equipment Items and Tasks**} \\ \cline{2-7}  & **8** & **9** & **10** & **11** & **12** & **13** \\ \hline GA (0.6,0.4) & 0.905917 & 0.910875 & 0.901094 & 0.937174 & 0.937185 & 0.936331 \\ \hline IGA (0.8,0.1,0.1) & 0.905917 & 0.910875 & 0.901097 & 0.937207 & 0.937478 & 0.936362 \\ \hline IGA (0.7,0.2,0.1) & 0.905917 & 0.910875 & 0.901097 & 0.937207 & 0.937478 & 0.936362 \\ \hline IGA (0.6,0.2,0.2) & 0.905917 & 0.910875 & 0.901097 & 0.937207 & 0.937478 & 0.936362 \\ \hline IGA (0.5,0.3,0.2) & 0.905917 & 0.910875 & 0.901097 & 0.937207 & 0.937478 & 0.936362 \\ \hline IGA (0.4,0.3,0.3) & 0.905917 & 0.910875 & 0.901097 & 0.937207 & 0.937478 & 0.936362 \\ \hline IGA (0.3,0.3,0.4) & 0.905917 & 0.910875 & 0.901097 & 0.937207 & 0.937478 & 0.936362 \\ \hline PSO-GA (0.6) & 0.905898 & 0.910849 & 0.901045 & 0.936897 & 0.937376 & 0.936174 \\ \hline GA-PSO (0.3,0.1,0.6) & 0.905917 & 0.910875 & 0.901097 & 0.937207 & 0.937458 & 0.936362 \\ \hline GA-PSO (0.1,0.2,0.7) & 0.905917 & 0.910875 & 0.901097 & 0.93715 & 0.937458 & 0.936349 \\ \hline GA-PSO (0.6,0.2,0.2) & 0.905917 & 0.910875 & 0.901097 & 0.937178 & 0.937478 & 0.936349 \\ \hline DPSO (0.2,0.3,0.5) & 0.905917 & 0.910875 & 0.901097 & 0.937207 & 0.937444 & 0.936354 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Multi-algorithm comprehensive evaluation value comparison table. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm and Parameter**} & \multicolumn{6}{c}{**Number of Equipment Items and Tasks**} \\ \cline{2-7}  & **8** & **9** & **10** & **11** & **12** & **Total** \\ \hline GA (0.6,0.4) & 1666 & 3932 & 6101 & 10,239 & 24,789 & 46,727 \\ \hline IGA (0.8,0.1,0.1) & 2391 & 3079 & 4455 & 4821 & 5936 & 20,682 \\ \hline IGA (0.7,0.2,0.1) & 1503 & 1926 & 2419 & 3216 & 4234 & 13,298 \\ \hline IGA (0.6,0.2,0.2) & 1100 & 1397 & 1848 & 2193 & 2944 & 9482 \\ \hline IGA (0.5,0.3,0.2) & 840 & 1053 & 1413 & 1652 & 2089 & 7047 \\ \hline IGA (0.4,0.3,0.3) & 653 & 843 & 1192 & 1390 & 1742 & 5820 \\ \hline IGA (0.3,0.3,0.4) & 576 & 699 & 923 & 1123 & 1418 & 4739 \\ \hline PSO-GA (0.6) & 1188 & 1974 & 4049 & 8285 & 7990 & 23,486 \\ \hline GA-PSO (0.3,0.1,0.6) & 894 & 1131 & 2153 & 2357 & 4913 & 11,448 \\ \hline GA-PSO (0.1,0.2,0.7) & 625 & 1212 & 1676 & 2185 & 3463 & 9161 \\ \hline GA-PSO (0.6,0.2,0.2) & 666 & 1236 & 2140 & 2935 & 5261 & 12,238 \\ \hline DPSO (0.2,0.3,0.5) & 432 & 767 & 1136 & 1528 & 2123 & 5986 \\ \hline DPSO (0.3,0.2,0.5) & 495 & 1071 & 1291 & 2070 & 2330 & 7257 \\ \hline DPSO (0.1,0.2,0.7) & 515 & 1047 & 1364 & 1972 & 2398 & 7296 \\ \hline DPSO (0.5,0.1,0.4) & 582 & 1362 & 2089 & 2856 & 3996 & 10,885 \\ \hline DPSO (0.4,0.1,0.5) & 628 & 1354 & 1844 & 2747 & 3974 & 10,547 \\ \hline DPSO (0.3,0.1,0.6) & 540 & 1464 & 1763 & 2623 & 3754 & 10,144 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Multi-algorithm parameter experiment number of iteration data table.