# A Discrete Particle Swarm Optimization Algorithm for Dynamic Scheduling of Transmission Tasks
## 4 Experiments
### Summary of Experimental Analysis  
Within the range of the number of equipment and time periods that can be verified by the enumeration algorithm, and based on an analysis of success, the DPSO and the IGA are effective, and the approximate rates can calculate the global-optimal solution, while the GR is less effective. Within the range of the number of equipment and time periods that cannot be verified by the enumeration algorithm, the DPSO can calculate and obtain a global-optimal solution in most cases based on the disaster-recovery-backup idea and the analysis of the algorithm itself. As the number of equipment increases, the effectiveness of IGA algorithms significantly decreases.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Eq.**} & \multirow{2}{*}{**GR**} & \multirow{2}{*}{**IGA**} & \multicolumn{4}{c}{**DPSO**} \\ \cline{4-5}  & & & **[min, max]** & **Avg.** & **Stdea.** \\ \hline
11 & 0.873344 & 0.896935 & [0.898354, 0.898388] & 0.898379 & 0.000014 \\
12 & 0.878253 & 0.902085 & [0.903259, 0.90338] & 0.9033514 & 0.000052 \\
13 & 0.878391 & 0.914041 & [0.916594, 0.916635] & 0.9166176 & 0.000018 \\
14 & 0.904408 & 0.931707 & [0.934612, 0.934725] & 0.9346716 & 0.000048 \\
15 & 0.891888 & 0.917394 & [0.920402, 0.920621] & 0.920569 & 0.000094 \\ Avg. & 0.874780 & 0.899094 & [0.900205, 0.900242] & 0.9002276 & 0.000018 \\ \hline \hline \end{tabular}
\end{table}
Table 8: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Eq.**} & \multirow{2}{*}{**IGA**} & \multicolumn{4}{c}{**DPSO**} \\ \cline{3-4}  & & **[min, max]** & **Avg.** & **Stdea.** \\ \hline
4 & 643 & [105, 132] & 116.4 & 10.0 \\
5 & 2692 & [340, 552] & 446.8 & 88.4 \\
6 & 7452 & [1674, 2088] & 1812.4 & 179.8 \\
7 & 15,095 & [4355, 9186] & 5893.8 & 1963.8 \\
8 & 74,431 & [7719, 11,447] & 9395.8 & 1465.2 \\
9 & 266,112 & [36,169, 69,736] & 48,312.8 & 13,134.6 \\
10 & 421,878 & [42,925, 88,540] & 63,079 & 16,922.1 \\
11 & 899,103 & [108,956, 208,574] & 132,676.2 & 42,597.5 \\
12 & 1,210,932 & [209,287, 407,190] & 309,613.4 & 90,217.6 \\
13 & 2,211,539 & [362,240, 765,968] & 498,789 & 166,816.1 \\
14 & 3,241,904 & [493,569, 1,381,226] & 841,163.8 & 328,465.6 \\
15 & 4,141,849 & [735,817, 2,595,418] & 1,297,816.8 & 746,327.9 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Multi-algorithm iteration number comparison table.  
Based on the analysis of the evaluation values, the evaluation values calculated by the DPSO and the IGA are close, while the GR's calculation results differ greatly from those calculated by the two intelligent algorithms. In all equipment-count tests, the DPSO achieved higher evaluation values than the IGA. This indicates that the accuracy of the DPSO is consistently higher than that of the IGA.  
By analyzing the efficiency of the two intelligent algorithms through the number of iterations, the execution efficiency of the IGA differs greatly from that of the DPSO.  
The advantages and disadvantages of various algorithms and comparison algorithms proposed in this paper are shown in Table 10.  
In comparative analyses, the DPSO algorithm is comprehensively evaluated to be the best.