基于改进NSGA-Ⅱ的多目标无人机集群任务优化方法OA

UAV swarms are widely used in tasks such as personnel search and rescue,as well as military reconnaissance.In order to improve the efficiency of unmanned cluster in carrying out large-scale reconnaissance tasks,a multi-objective optimi-zation model is constructed to minimize the flight time and maximize the detection revenue for the task allocation problem of UAV cluster with different sensors.By constructing integer task encoding and a population initialization method based on Voronoi partitioning,the quality of the initial solution is improved,and the genetic method in NSGA-II algorithm is restricted to shorten the optimization time.This algorithm can provide a set of non-dominated solutions,allowing for the selection of the shortest flight time or maximum profit plan based on preference.To cope with large-scale damage,an initial population is generated based on local task flow rules to achieve rapid task optimization.Simulation results show that compared to the orig-inal algorithm,the improved algorithm has significant advantages in task allocation and damage reconstruction of large-scale unmanned clusters.