指挥控制与仿真2026，Vol.48Issue(1)：28-35,8.DOI:10.3969/j.issn.1673-3819.2026.01.004
基于改进NSGA-Ⅱ的多目标无人机集群任务优化方法
Multi-objective task optimization for UAV swarm based on improved NSGA-Ⅱ
刘兆才 1刘杰2
作者信息
- 1. 华中科技大学,湖北 武汉 430074
- 2. 中国人民解放军 91388 部队,广东 湛江 524000
- 折叠
摘要
Abstract
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.关键词
多目标优化/无人机集群/任务分配/NSGA-Ⅱ算法Key words
multi-objective optimization/UAV swarm/task allocation/NSGA-Ⅱ Algorithm分类
航空航天引用本文复制引用
刘兆才,刘杰..基于改进NSGA-Ⅱ的多目标无人机集群任务优化方法[J].指挥控制与仿真,2026,48(1):28-35,8.
