# A Discrete Particle Swarm Optimization Algorithm for Dynamic Scheduling of Transmission Tasks
## 4 Experiments
### Simulation Experiment  
This section compares the advantages and disadvantages of several algorithms by simulating the actual situation of the transmitting station. The experimental program writing tool used is Visual Studio 2012, and the language used is C++ with MFC architecture. The hardware environment used is a Microsoft Surface X1 portable computer, the CPU used is Intel Core i7 3.60 GHz, the memory used is 16 GB, and the operating system used is Win10. In the actual working environment of the transmission station, the number of equipment is usually fixed, and the number of time periods varies according to the program settings. In this part of the simulation experiment, the parameters with the best performance of the algorithm are selected for the simulation experiment. The enumeration algorithm was abandoned due to the long execution time after the number of tasks increased, and the GR had no parameters. The IGA parameters are (0.8,0.1,0.1) for the simulation experiments, and the DPSO selected parameters (0.3,0.1,0.6) for the simulation experiments. The fixed value of the number of equipment is 10, and the number of time periods is 24 h per day, with one time period every half an hour, that is, a [4; 48] interval. The two intelligent algorithms are tested with the same number of iterations. Because there is no optimal-value comparison, the success-rate data cannot be compared. The simulation experiment only compares the algorithm's accuracy with the average value of the comprehensive evaluation value obtained 100 times. The data obtained are in Table 11. \begin{table}
\begin{tabular}{c c c c c} \hline
**Eq.** & **Availability** & **Accuracy** & **Efficiency** & **Evaluation** \\ \hline ENU & high & N/A & N/A & bad \\ GR & low & low & N/A & bad \\ IGA & middle & high & low & better \\ DPSO & high & high & high & best \\ \hline \end{tabular}
\end{table} 
Table 10: Comparison of advantages and disadvantages of multiple algorithms. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Periods** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline GR & 0.893972 & 0.901704 & 0.913778 & 0.881862 & 0.879343 & 0.889435 & 0.906143 & 0.884089 & 0.896052 \\ IGA & 0.917416 & 0.92164 & 0.934022 & 0.90367 & 0.904581 & 0.907794 & 0.932187 & 0.911542 & 0.920281 \\ DPSO & 0.918031 & 0.922333 & 0.935052 & 0.904655 & 0.905095 & 0.909001 & 0.933586 & 0.912695 & 0.921244 \\ \hline
**Periods** & **13** & **14** & **15** & **16** & **17** & **18** & **19** & **20** & **21** \\ \hline GR & 0.893406 & 0.881768 & 0.89654 & 0.907105 & 0.89874 & 0.860929 & 0.864331 & 0.894956 & 0.8873 \\ IGA & 0.921019 & 0.910778 & 0.916936 & 0.93331 & 0.920762 & 0.88272 & 0.890827 & 0.921293 & 0.914671 \\ DPSO & 0.922169 & 0.911839 & 0.91786 & 0.93453 & 0.921716 & 0.883748 & 0.891933 & 0.922291 & 0.915707 \\ \hline \end{tabular}
\end{table} 
Table 11: Accuracy comparison data table of three algorithms simulation experiments. According to Table 11, the DPSO has obtained an evaluation value superior to that of the IGA, which shows that the two intelligent algorithms operate stably, and that the DPSO has obvious accuracy advantages when dealing with DSTTs. See Figure 4 for a comparison diagram. In the simulation experiments, select 100 experimental data with the largest number of time periods, that is, 48 time periods, and observe the evaluation value and algorithm calculation time of each of the two algorithms. The data are presented in Figure 5.