# Research on Transmission Task Static Allocation Based on Intelligence Algorithm
## 4 Experiments
### Algorithm Experiments
\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 DPSO (0.3,0.2,0.5) & 0.905917 & 0.910875 & 0.901097 & 0.937207 & 0.937386 & 0.936362 \\ \hline DPSO (0.1,0.2,0.7) & 0.905917 & 0.910875 & 0.901097 & 0.937207 & 0.937466 & 0.936362 \\ \hline DPSO (0.5,0.1,0.4) & 0.905917 & 0.910875 & 0.901097 & 0.937207 & 0.937478 & 0.936362 \\ \hline DPSO (0.4,0.1,0.5) & 0.905917 & 0.910875 & 0.901097 & 0.937207 & 0.937478 & 0.936362 \\ \hline DPSO (0.3,0.1,0.6) & 0.905917 & 0.910875 & 0.901097 & 0.937207 & 0.937478 & 0.936362 \\ \hline \multicolumn{7}{c}{**Number of Equipment Items and Tasks**} \\ \cline{2-7}  & **14** & **15** & **16** & **17** & **18** & **19** \\ \hline GA (0.6,0.4) & 0.920982 & 0.927141 & 0.935498 & 0.937482 & 0.949289 & 0.925388 \\ \hline IGA (0.8,0.1,0.1) & 0.921115 & 0.927263 & 0.936067 & 0.938276 & 0.949914 & 0.925894 \\ \hline IGA (0.7,0.2,0.1) & 0.921115 & 0.927266 & 0.936067 & 0.938287 & 0.949892 & 0.925843 \\ \hline IGA (0.6,0.2,0.2) & 0.921115 & 0.927269 & 0.936067 & 0.938286 & 0.949935 & 0.925872 \\ \hline IGA (0.5,0.3,0.2) & 0.921115 & 0.927269 & 0.936067 & 0.938267 & 0.949858 & 0.92572 \\ \hline IGA (0.4,0.3,0.3) & 0.921115 & 0.927266 & 0.936046 & 0.938245 & 0.949771 & 0.925716 \\ \hline IGA (0.3,0.3,0.4) & 0.921078 & 0.927269 & 0.936063 & 0.938189 & 0.949753 & 0.925521 \\ \hline PSO-GA (0.6) & 0.920734 & 0.92711 & 0.935687 & 0.938034 & 0.949794 & 0.925663 \\ \hline GA-PSO (0.3,0.1,0.6) & 0.921099 & 0.92726 & 0.935907 & 0.938218 & 0.949846 & 0.925759 \\ \hline GA-PSO (0.1,0.2,0.7) & 0.921094 & 0.927193 & 0.935904 & 0.938224 & 0.949888 & 0.925741 \\ \hline GA-PSO (0.6,0.2,0.2) & 0.921027 & 0.927247 & 0.936019 & 0.93824 & 0.949925 & 0.92585 \\ \hline DPSO (0.2,0.3,0.5) & 0.92102 & 0.927258 & 0.936053 & 0.938287 & 0.949983 & 0.925886 \\ \hline DPSO (0.3,0.2,0.5) & 0.921103 & 0.927269 & 0.936067 & 0.938287 & 0.949983 & 0.925906 \\ \hline DPSO (0.1,0.2,0.7) & 0.921115 & 0.927268 & 0.936067 & 0.938287 & 0.949978 & 0.925906 \\ \hline DPSO (0.5,0.1,0.4) & 0.921115 & 0.927269 & 0.936067 & 0.938287 & 0.949983 & 0.925906 \\ \hline DPSO (0.4,0.1,0.5) & 0.921115 & 0.927269 & 0.936067 & 0.938287 & 0.949983 & 0.925906 \\ \hline DPSO (0.3,0.1,0.6) & 0.921115 & 0.927269 & 0.936067 & 0.938287 & 0.949983 & 0.925906 \\ \hline \multicolumn{7}{c}{**Number of Equipment Items and Tasks**} \\ \cline{2-7}  & **20** & **21** & **22** & **23** & **24** & **25** \\ \hline GA (0.6,0.4) & 0.924698 & 0.93266 & 0.948151 & 0.945635 & 0.937196 & 0.932849 \\ \hline IGA (0.8,0.1,0.1) & 0.942251 & 0.940722 & 0.940895 & 0.948123 & 0.936434 & 0.945138 \\ \hline IGA (0.7,0.2,0.1) & 0.942251 & 0.940707 & 0.940848 & 0.948091 & 0.936418 & 0.945083 \\ \hline IGA (0.6,0.2,0.2) & 0.942251 & 0.940727 & 0.94086 & 0.948109 & 0.936401 & 0.945047 \\ \hline IGA (0.5,0.3,0.2) & 0.942251 & 0.940711 & 0.9408 & 0.948031 & 0.936405 & 0.945035 \\ \hline IGA (0.4,0.3,0.3) & 0.942249 & 0.940695 & 0.940781 & 0.948053 & 0.936265 & 0.944858 \\ \hline IGA (0.3,0.3,0.4) & 0.942251 & 0.940613 & 0.940735 & 0.947955 & 0.936355 & 0.944666 \\ \hline PSO-GA (0.6) & 0.942028 & 0.940462 & 0.940715 & 0.948021 & 0.936212 & 0.944911 \\ \hline GA-PSO (0.3,0.1,0.6) & 0.942157 & 0.940702 & 0.940873 & 0.94802 & 0.936288 & 0.94493 \\ \hline GA-PSO (0.1,0.2,0.7) & 0.942062 & 0.940581 & 0.940739 & 0.947989 & 0.936225 & 0.944912 \\ \hline GA-PSO (0.6,0.2,0.2) & 0.942084 & 0.940555 & 0.94081 & 0.948079 & 0.936324 & 0.945145 \\ \hline DPSO (0.2,0.3,0.5) & 0.94223 & 0.94071 & 0.940962 & 0.9481 & 0.936462 & 0.945255 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._According to the average value in the above table, a comparison chart of multi-algorithm multi-parameter array can be drawn (Figure 5). According to the experiment, it can be seen that the evaluation value obtained by GA is the lowest, followed by PSO-GA, and the best experimental result is DPSO.