# Task Assignment of UAV Swarm Based on Wolf Pack Algorithm
## 5 Conclusions  
Aiming at the problem of the UAV swarm task assignment, this paper introduces a novel swarm intelligence algorithm, the wolf pack algorithm (WPA), which is particularly suitable for high-dimensional continuous optimization problems. In order to apply this method to the discrete optimization problem and improve the convergence speed, this paper proposes the PSO-GA-DWPA  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{5}{c}{**Cost Function**} & \multicolumn{5}{c}{**Convergence Time**} \\ \cline{3-10}  & & & **Maximum** & **Minimum** & **Mean** & **Standard** & **Maximum** & **Minimum** & **Mean** & **Standard** \\  & & & & & & & & & & **Deviation** \\ \hline \multirow{2}{*}{5 vs. 8} & PSO & 31.69 & 28.72 & 29.49 & 0.78 & 46.77 & 16.23 & 36.31 & 7.8 \\  & WPA & 29.7 & 28.72 & 28.92 & 0.37 & 7.47 & 2.64 & 4.22 & 1.43 \\  & PSO-GA-WPA & 30.98 & 28.72 & 29.23 & 0.52 & 4.36 & 1.45 & 2.53 & 0.17 \\ \hline \multirow{2}{*}{20 vs. 30} & PSO & 582.86 & 412.4 & 511.84 & 37 & - & - & - & - \\  & WPA & 279.15 & 293.23 & 261.95 & 12.65 & 100.55 & 64.89 & 70.29 & 10.17 \\  & PSO-GA-WPA & 269.84 & 235.28 & 253.6 & 10.2 & 51 & 23.67 & 29.56 & 4.77 \\ \hline \multirow{2}{*}{100 vs. 150} & PSO & 8586 & 7929 & 8212 & 225 & - & - & - & - \\  & WPA & 1908 & 1650 & 1783 & 80 & 2508 & 2160 & 2255 & 102 \\ \hline \multirow{2}{*}{100 vs. 150} & PSO-GA-WPA & 1880 & 1480 & 1606 & 78 & 1894 & 1784 & 1844 & 27 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of simulation results.  
Figure 12: Mean of the Monte Carlo runs: 100 vs. 150 scenario, minimum cost within specified time.  
based on matrix coding and the principles of particle swarm optimization and genetic algorithm. According to a general task assignment model and simulation experiments, this method proved to be applicable for high-dimensional task assignment.  
The simulation results show that in the case of a small dimension such as Scenario 1, the PSO-GA-DWPA has almost the same optimization ability as the PSO. However, when the dimension increases to 30 in Scenario 2, the PSO-GA-DWPA is obviously superior to PSO both in terms of convergence speed and solution accuracy. When the dimension increases to 150 in Scenario 3, the PSO fails, while the PSO-GA-DWPA can still obtain an effective solution. Therefore, this method can serve as a useful reference for the UAV swarm task assignment.  
Conceptualization, Y.L.; methodology, Y.L.; software, Y.L.; validation, Y.L., Y.M. and J.W.; formal analysis, Y.L.; investigation, Y.L.; resources, Y.L., Y.M., J.W. and L.H.; data curation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L.; visualization, Y.L.; supervision, Y.M., J.W. and L.H.; project administration, Y.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to thank the anonymous reviewers for their constructive comments and suggestions, which improved the quality of the paper.  
The authors declare no conflict of interest.