# A Discrete Particle Swarm Optimization Algorithm for Dynamic Scheduling of Transmission Tasks
## 4 Experiments  
### Comparison Algorithm and Method  
The iteration number of each algorithm in this part is limited by Formula (23). The comparison algorithms can be found in Table 6.  
According to the characteristics of the DSTT, the algorithm-comparison test uses the enumeration algorithm as the reference algorithm in the range of [4; 7] between the number of equipment and the number of time periods. The enumeration algorithm takes a long time to produce its calculations, but it can clearly obtain the global-optimal solution. With the optimal solution as a comparison reference, the performance index of the algorithm is evaluated by recording the number of iterations required by the other algorithms to reach the optimal solution. In the interval where the number of equipment and periods exceeds seven, there is no optimal solution for reference. The comparison method adopts two calculations. Each comparison is completed with each parameter group first. The comparison results show that the calculation results of more than two groups of parameters are completely consistent, and the result is determined to be the global best solution. Then, the second calculation is completed to obtain the number of iterations of the algorithm for each parameter to obtain the global-best solution so as to evaluate the algorithm's performance. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Explain** \\ \hline ENU & Enumeration algorithm: The global-optimal solution can be obtained by traversing the running chart of all tasks and calculating the evaluation value. With the increase in the number of equipment and time periods, the iteration number increases rapidly and the algorithm’s execution time is long. \\ GR & Greedy algorithm: Find out the tasks that can obtain the best allocation among all tasks. According to this principle, until all tasks are allocated, the algorithm’s execution time is stable, the number of algorithm iterations is related to the task period, and the conflicts encountered in the allocation are backtracked [4]. \\  & Improved genetic algorithm: Select the elitist-retention strategy, the discontinuous cycle replacement group crossover strategy for crossover, and the overall equipment task switching strategy for mutation. The three parameters are set as the algorithm’s optimal parameter array, with a selection factor = 0.8, a crossover factor = 0.1, and a mutation factor = 0.1. Refer to previous research results for the selection of parameters [3]. \\ DPSO & Discrete particle swarm optimization algorithm: The parameters are calculated according to the five sets of parameters in Table 5, and the corresponding statistical calculations are performed. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: DSTT comparison algorithm list. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Parameter**} & \multicolumn{2}{c}{**[4; 7]**} & **[4; 12]** \\ \cline{2-6}  & **IRF** & **PBF** & **GBF** & **Success Rate** & **Weighted Iteration** & **Maximum Number of Times** \\ \hline DPSO1 & 0.5 & 0.1 & 0.4 & 100\% & 2.1828 & 6 \\ DPSO2 & 0.4 & 0.1 & 0.5 & 100\% & 1.7931 & 6 \\ DPSO3 & 0.3 & 0.1 & 0.6 & 99.75\% & 2.0399 & 5 \\ DPSO4 & 0.2 & 0.1 & 0.7 & 100\% & 1.9032 & 5 \\ DPSO5 & 0.2 & 0.2 & 0.6 & 100\% & 1.4791 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comprehensive comparison table of parameter tests of DPSO. In order not to lose fairness, all algorithms in the test experiment in this section use the same initialization task queue for their calculations. Each task number randomly generates 100 task sequences for the best-scheduling calculations; compares the success rate of each algorithm in calculating the global-best solution under the above conditions; calculates the iteration times of the algorithm in the global-best solution; and calculates the proportional cumulative evaluation value, which represents the average of the calculated comprehensive evaluation value of the best allocation.