# A Discrete Particle Swarm Optimization Algorithm for Dynamic Scheduling of Transmission Tasks
## 4 Experiments
### Algorithm Comparison Experiment  
This section compares the effectiveness, accuracy, and efficiency of several algorithms in the experiments.  
The number of successes in obtaining the global best solution from the experimental calculations are shown in Table 7.  
When the number of equipment is greater than 7, the ENU is no calculated due to too long time. The DPSO uses the five sets of parameters in Table 5 to calculate the success numbers and performs statistical calculations on the maximum, minimum, average, and mean square deviation of the five sets of result data. It can be seen that the DPSO's algorithm data are stable, but data stability decrease as the number of equipment increases. Comparing algorithms, the DPSO is superior to the IGA, and the calculation results of the two intelligent algorithms are far superior to that of the GR.  
The average comparison of the evaluation values calculated by the algorithms is shown in Table 8.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Eq.**} & \multirow{2}{*}{**ENU**} & \multirow{2}{*}{**GR**} & \multirow{2}{*}{**IGA**} & \multicolumn{3}{c}{**DPSO**} \\ \cline{5-8}  & & & & **[min, max]** & **Avg.** & **Stdea.** \\ \hline
4 & 100 & 25 & 100 & [100, 100] & 100 & 0.00000 \\
5 & 100 & 8 & 100 & [100, 100] & 100 & 0.000000 \\
6 & 100 & 5 & 100 & [99, 100] & 99.8 & 0.447214 \\
7 & 100 & 2 & 99 & [98, 100] & 99.6 & 0.894427 \\
8 & N/A & 1 & 89 & [99, 100] & 99.8 & 0.447214 \\
9 & N/A & 0 & 70 & [92, 99] & 97.2 & 2.949576 \\
10 & N/A & 0 & 62 & [93, 100] & 97.8 & 2.774887 \\
11 & N/A & 0 & 35 & [93, 98] & 96.8 & 2.167948 \\
12 & N/A & 0 & 39 & [83, 97] & 93.2 & 5.932959 \\
13 & N/A & 0 & 20 & [90, 100] & 94.8 & 3.701351 \\
14 & N/A & 0 & 4 & [77, 97] & 88.6 & 8.049845 \\
15 & N/A & 0 & 10 & [67, 94] & 87.4 & 11.436783 \\ Total & 400 & 41 & 728 & [1124, 1182] & 1155 & 26.870058 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Multi-algorithm success number comparison table.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Eq.**} & \multirow{2}{*}{**GR**} & \multirow{2}{*}{**IGA**} & \multicolumn{3}{c}{**DPSO**} \\ \cline{5-8}  & & & **[min, max]** & **Avg.** & **Stdea.** \\ \hline
4 & 0.815214 & 0.838134 & [0.838134, 0.838134] & 0.838134 & 0.000000 \\
5 & 0.874183 & 0.895642 & [0.895642, 0.895642] & 0.895642 & 0.000000 \\
6 & 0.856763 & 0.883459 & [0.883458, 0.883459] & 0.8834588 & 0.000000 \\
7 & 0.899497 & 0.916141 & [0.916114, 0.916156] & 0.9161528 & 0.000007 \\
8 & 0.892468 & 0.913266 & [0.91355, 0.913556] & 0.9135548 & 0.000003 \\
9 & 0.858319 & 0.881545 & [0.882189, 0.882289] & 0.8822652 & 0.000043 \\
10 & 0.874637 & 0.898784 & [0.89988, 0.899956] & 0.8999352 & 0.000031 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison table of evaluation values of multi-algorithms.  
According to the data in Table 8, the evaluation-value data calculated by the DPSO algorithm are stable, indicating that the algorithm has high stability. Compared to the IGA, when the number of equipment is small, the accuracy of the two intelligent algorithms is equivalent. As the number of equipment increases, the accuracy advantage of the DPSO significantly increases. The calculation results of the GR algorithm differ greatly from those of the two intelligent algorithms.  
The comparison results of the number of iterations to obtain the best solution are shown in Table 9.  
This research uses the number of iterations to evaluate the efficiency of the algorithm, because intelligent algorithms need to calculate the results of the fitness function for each iteration, and generally the algorithm's operation time is much lower than the calculation time of the fitness function. Table 9 shows the statistical analysis of the iterations of the two intelligent algorithms. It can be seen that the iterations of the DPSO are far superior to the efficiency of the IGA. Because the DPSO is a cluster search and the number of iterations of the algorithm includes randomness, there is a large gap in the data statistics of DPSO algorithms.