# Multi-Population Parallel Wolf Pack Algorithm for Task Assignment of UAV Swarm
## 5 Conclusions  
With the aim of reducing the optimization time for the problem of UAV swarm task assignment, the multi-population parallel Wolf Pack Algorithm (MPPWPA) was proposed. According to building of the task assignment model and the analysis of the WPA, the multi-population optimization method, the communication structures and the parallel propulsion mode, this paper proposed the following methods: (a) an elite-mass population distribution to optimize better solutions twice; (b) a pretreatment of the population to increase population diversity; (c) an approximate average division method to equalize the quality of all sub-populations; (d) a parallel optimization structure to realize the MPPWPA.  
Firstly, the proposed algorithm was verified as reasonable by means of simulation of a problem with an eight-dimensional solution space, and it showed good performance in for the problem with high dimensional variables. Then, we studied the influence of its control parameters: \(Num\) and \(P_{m}\). The results of simulation experiments indicated that (1) when the dimension of the solution space was low, MPPWPA with different \(Num\) and \(P_{m}\) could obtain the best solution; (2) when the dimension of the solution space was high, \(Num\) and \(P_{m}\) had little effect on the convergence time, but they did not change the overall effectiveness of the algorithm; (3) when the dimension of the solution space was very high, the MPPWPA had satisfying performance, especially in terms of convergence time; (4) in order to obtain a better optimization performance, the size of sub-population needed to match the dimensions of the variable.  
The parallel computing in this paper was implemented on a single computer, and future work will focus on the feasibility of multi-computer distributed parallel computing or cloud computing to solve large-scale task assignment. In addition, we set the \(P_{m}\) as a constant in this work; we will study variable migration probabilities in future work. In the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Objective Function Value**} & \multicolumn{2}{c}{**Convergence Time**} \\ \cline{2-5}  & **Ave.** & **Std.** & **Ave.** & **Std.** \\ \hline PSO & 8268 & 218 & 5579 & 1464 \\ GA & 2390 & 53 & 2581 & 503 \\ ABC & 2455 & 134 & 4643 & 644 \\ WPA & 1789 & 90.04 & 3262 & 753 \\ MPPWPA & **1552** & 50.49 & **1242** & 371 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Objective function value and convergence time in scenario 3.  
Figure 20: Average objective function values in scenario 3.  
longer term, we also need to consider how to handle moving targets. Whether to track the target according to the original assignment result or to re-assign the task is a question that is worthy of further study.  
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. and J.W.; data curation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L. and Y.M.; visualization, Y.L.; supervision, Y.M. and J.W.; project administration, Y.M.; funding acquisition, Y.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Science and Technology on Electro-optic Control Laboratory and the Aviation Science Fund, grant number 20175151024.  
Not applicable.  
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.