Multiple UAVs reconnaissance task allocation based on improved wolf pack algorithm

The large-scale reconnaissance task allocation problem of multiple unmanned aerial vehicles (UAVs) is researched. In order to simplify the problem, the task allocation process is divided into two independent stages: the cluster stage and the reconnaissance sequence allocation stage. In the first stage, a k-means cluster algorithm based on the azimuth angle is developed to group a large number of targets. The traditional wolf pack algorithm (WPA) does not fit the task allocation problem. In the second stage, an improved WPA is developed to decide the reconnaissance sequence of every group of targets. The improved WPA mainly modifies three behaviors in the traditional WPA, namely, wandering behavior, summoning behavior, and besieging behavior. Two simulation experiments are carried out. In the first experiment the algorithm in this paper is applied. In the second experiment the traditional k-means cluster based on the distance is applied to group the targets. The comparison of experiment results validates that the algorithm in this paper is more effective and outstanding.