# Research on Transmission Task Static Allocation Based on Intelligence Algorithm
## 4 Experiments
### Algorithm Experiments
The number of iterations when the algorithm calculates is recorded to obtain the global optimal solution (if the optimal solution is not obtained, count according to the upper limit of the number of iterations set by the algorithm), and the number of iterations of multiple algorithms is obtained (Table 8). According to the above table, the comparison diagram can be drawn (Figure 7). Analysis of the experimental data:  
1. The number of iterations of all algorithms increases gradually with the number of tasks. 2. The number of iterations of the GA calculation increases rapidly because of the low success rate. In the experiment, it is almost impossible to obtain the optimal solution using the set number of iterations. The number of iterations of PSO-GA and GA-PSO is equal, which shows that both hybrid strategies can effectively improve the convergence of intelligent algorithms. The IGA calculation results are superior to those of the hybrid algorithms. The advantage of the number of iterations of the DPSO calculation is obvious, which proves that the convergence of DPSO is the highest. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm and Parameter**} & \multicolumn{8}{c}{**Number of Equipment Items and Tasks**} \\ \cline{2-9}  & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **15** & **16** \\ \hline GA (0.6,0.4) & 2226 & 4544 & 7299 & 10,334 & 22,411 & 22,703 & 37,218 & 52,963 & 78,680 \\ \hline IGA (0.8,0.1,0.1) & 2152 & 3206 & 3829 & 4906 & 5998 & 6733 & 8202 & 11,879 & 12,107 \\ \hline IGA (0.6,0.2,0.2) & 1016 & 1397 & 1767 & 2390 & 2519 & 3247 & 3910 & 4570 & 5502 \\ \hline PSO-GA (0.6) & 1407 & 2054 & 4339 & 5549 & 11,127 & 11,115 & 20,909 & 21,067 & 41,885 \\ \hline GA-PSO (0.3,0.1,0.6) & 682 & 1282 & 1892 & 2424 & 4489 & 4743 & 7662 & 8182 & 21,488 \\ \hline GA-PSO (0.6,0.2,0.2) & 639 & 984 & 1559 & 3582 & 3776 & 6524 & 9717 & 15,660 & 22,344 \\ \hline DPSO (0.5,0.1,0.4) & 699 & 1226 & 2072 & 2967 & 4438 & 5397 & 7962 & 9579 & 12,810 \\ \hline DPSO (0.4,0.1,0.5) & 622 & 1303 & 1910 & 2879 & 3651 & 4895 & 6265 & 8283 & 10,733 \\ \hline DPSO (0.3,0.1,0.6) & 644 & 1237 & 1874 & 2613 & 3310 & 4407 & 6222 & 7747 & 10,097 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm and Parameter**} & \multicolumn{8}{c}{**Number of Equipment Items and Tasks**} \\ \cline{2-9}  & **17** & **18** & **19** & **20** & **21** & **22** & **23** & **24** & **25** \\ \hline GA (0.6,0.4) & 125,956 & 155,587 & 168,004 & 184,432 & 299,930 & 354,558 & 398,367 & 466,670 & 532,595 \\ \hline IGA (0.8,0.1,0.1) & 20,016 & 26,127 & 29,061 & 18,695 & 29,897 & 73,513 & 46,724 & 59,135 & 125,374 \\ \hline IGA (0.6,0.2,0.2) & 8712 & 19,732 & 22,638 & 9329 & 23,594 & 76,462 & 33,057 & 52,605 & 150,233 \\ \hline PSO-GA (0.6) & 40,362 & 52,858 & 83,780 & 70,156 & 98,626 & 137,713 & 110,321 & 137,800 & 222,637 \\ \hline GA-PSO (0.3,0.1,0.6) & 25,750 & 44,814 & 56,354 & 40,915 & 48,824 & 94,523 & 104,378 & 123,837 & 201,774 \\ \hline GA-PSO (0.6,0.2,0.2) & 19,774 & 43,101 & 51,090 & 56,063 & 73,014 & 114,275 & 81,685 & 116,186 & 176,458 \\ \hline DPSO (0.5,0.1,0.4) & 18,052 & 20,118 & 26,519 & 24,658 & 31,859 & 45,711 & 53,620 & 64,200 & 67,979 \\ \hline DPSO (0.4,0.1,0.5) & 12,995 & 17,680 & 19,686 & 18,324 & 24,852 & 30,707 & 35,037 & 37,732 & 43,879 \\ \hline DPSO (0.3,0.1,0.6) & 13,612 & 14,993 & 17,979 & 17,297 & 21,105 & 27,875 & 30,236 & 32,038 & 45,693 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Multi-algorithm number of iterations comparison table.