Interference unmanned aerial vehicle deployment algorithm based on improved grey wolf algorithm

Abstract

In this study, an improved Grey Wolf Optimization (GWO) algorithm was designed to optimize the deployment of multiple interference unmanned aerial vehicles (UAVs). First, a scenario model was constructed based on an electromagnetic wave propagation model. Second, using multi-UAV multi-target interference as the task objective, an interference effectiveness evaluation function was built by introducing a low-coverage-efficiency reduction factor based on the global interference-to- noise ratio. An improved GWO algorithm with a reverse learning strategy was employed to solve the task optimization problem, and simulation experiments on interference UAV deployment tasks under different task pressures were conducted. The results show that, compared with the traditional GWO, the proposed algorithm exhibits a superior adaptability under different task pressures represented by the ratio of drones to targets: in high task pressure scenarios, the convergence speed of deployment schemes generated by the improved GWO algorithm has increased by 7.71%; under moderate and low task pressures, the stability of interference efficiency in the generated schemes has improved by nearly 30%; and the interference efficiency across different task pressures remains largely consistent. This demonstrates that the capability of improved GWO algorithm to accommodate interference UAV deployments across diverse task pressure scenarios, excelling particularly in high-pressure environments.