Multi-UAVs Cooperative Reconnaissance Task Allocation Under Heterogeneous Target Values

Abstract

This paper presents a Multiple Unmanned Aerial Vehicles (UAVs) cooperative reconnaissance task allocation model based on heterogeneous target value and proposes an improved Multi-Verse Optimizer (MVO) algorithm. Firstly, according to the reconnaissance value of the target, the reconnaissance targets are divided into high-value targets, low-value targets and decoy targets. It improves the authenticity of the problem. The purpose of task allocation is to maximize the reconnaissance revenue of UAVs as much as possible under the condition of minimizing the reconnaissance time and fuel loss of UAVs to the targets. Then, to solve the model, this paper improves the traditional MVO algorithm. Adaptive compression factor is introduced to improve the convergence speed of the algorithm. In addition, the differential mutation operation is performed in the wormhole movement stage to enhance the global search ability of the algorithm. The simulation results show that the improved algorithm can successfully solve the reconnaissance task allocation problem under different target values, and has obvious advantages in reconnaissance revenue and calculation speed compared with other methods.