# Research on Transmission Task Static Allocation Based on Intelligence Algorithm
## 4 Experiments
### Algorithm Parameter Test  
Before algorithm comparison and verification, the optimal parameter selection test of each algorithm is carried out. The experimental population size of the algorithm is calculated by 100, and the population size is kept unchanged during the iteration process. The upper limit of the number of iterations of the intelligent algorithm is set according to the following formula:  
\[\textit{IterationNumber}=\sum_{i=1}^{n}i^{2} \tag{25}\]  
The value of \(n\) is the number of equipment items and tasks.  
The algorithm parameter test is carried out using the comparison method of accurate calculation (enumeration algorithm). The parameters are selected based on the algorithm success rate (the proportion of the global optimal solution obtained by the algorithm through multiple calculations) and the algorithm convergence (after multiple calculations, the total number of iterations of the algorithm to obtain the global optimal solution is accumulated according to the upper limit of the number of iterations if the number of iterations reaches the upper limit, but still does not obtain the global optimal solution). The efficiency of intelligent algorithms depends on the calculation time of the fitness function, and the number of calculations of the fitness function is consistent in each iteration. Therefore, this paper uses the number of iterations to measure the efficiency of each intelligent algorithm. The test of the optimal parameters of each algorithm and the representative (all successful, fast convergence) parameter data are shown in Tables 4 and 5, which list the best parameter combinations of the intelligent algorithm parameter groups.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm and Parameter**} & \multicolumn{4}{c}{**Number of Equipment Items and Tasks**} & \multirow{2}{*}{**Average**} \\ \cline{2-2} \cline{4-6}  & \(8\) & \(9\) & \(10\) & \(11\) & \(12\) \\ \hline GA (0.6,0.4) & 100\% & 99\% & 100\% & 100\% & 88\% & 97.4\% \\ \hline IGA (0.8,0.1,0.1) & 100\% & 100\% & 100\% & 100\% & 100\% \\ \hline IGA (0.7,0.2,0.1) & 100\% & 100\% & 100\% & 100\% & 100\% \\ \hline IGA (0.6,0.2,0.2) & 100\% & 100\% & 100\% & 100\% & 100\% \\ \hline IGA (0.5,0.3,0.2) & 100\% & 100\% & 100\% & 100\% & 100\% \\ \hline IGA (0.4,0.3,0.3) & 100\% & 100\% & 100\% & 100\% & 100\% \\ \hline IGA (0.3,0.3,0.4) & 100\% & 100\% & 100\% & 100\% & 100\% \\ \hline PSO-GA (0.6) & 100\% & 99\% & 97\% & 94\% & 95\% & 97\% \\ \hline GA-PSO (0.3,0.1,0.6) & 100\% & 100\% & 100\% & 100\% & 100\% \\ \hline GA-PSO (0.1,0.2,0.7) & 100\% & 99\% & 100\% & 100\% & 99\% & 99.6\% \\ \hline GA-PSO (0.6,0.2,0.2) & 100\% & 100\% & 100\% & 99\% & 99\% & 99.6\% \\ \hline DPSO (0.2,0.3,0.5) & 100\% & 100\% & 100\% & 100\% & 100\% & 100\% \\ \hline DPSO (0.3,0.2,0.5) & 100\% & 100\% & 100\% & 100\% & 100\% & 100\% \\ \hline DPSO (0.1,0.2,0.7) & 100\% & 100\% & 100\% & 100\% & 100\% & 100\% \\ \hline DPSO (0.5,0.1,0.4) & 100\% & 100\% & 100\% & 100\% & 100\% & 100\% \\ \hline DPSO (0.4,0.1,0.5) & 100\% & 100\% & 100\% & 100\% & 100\% & 100\% \\ \hline DPSO (0.3,0.1,0.6) & 100\% & 100\% & 100\% & 100\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Multi-algorithm parameter experiment success rate data table.