Clustering optimization algorithm for large-scale unmanned aerial vehicle based on intelligent behavior of pigeons

Abstract

In future air battlefield, clusters of large-scale unmanned aerial vehicle (UAV) will become the dominant force. The effective grouping and clustering of large-scale UAV cluster systems are necessary steps to complete combat tasks. Due to the limited communication constraints in the battlefield, UAVs cannot obtain comprehensive and effective global combat information. Thus, this paper proposed a large-scale UAV clustering optimization algorithm based on the intelligent behavior of pigeons. Also, this paper studied and analyzed the intelligent behavior of the flock of pigeons with excellent navigation ability and mapped the hierarchical network mechanism in the flight process of the pigeon flock into the pigeon-inspired optimization (PIO) algorithm. Hence, it solved the problem of incomplete information in a limited interactive environment. On one hand, it is more effective and direct to guide the pigeon flock from adjacent individuals during flight. Therefore, under the limited interaction condition, the global optimal information of the basic PIO algorithm is replaced by the optimal individual information within the interaction range. On the other hand, the central position renewal of the pigeon flock consists of three parts: inertia part, imitation part, and environmental impact part. In order to verify the effectiveness of the improved PIO algorithm in a limited interactive range, this paper adopted three algorithms to cluster three datasets. Simulation results reveal that the improved PIO algorithm achieved significant improvement in the optimal solution and the average optimal solution; notably, the computation time does not increase significantly, thus providing an effective solution for the clustering of UAV cluster systems in the actual combat environment.