摘要
为提高无人机蜂群执行任务的效率,提高无人机蜂群与敌方对抗时获胜的概率,引入多种群机制和帕累托分配理论对多目标灰狼优化算法进行改进,使用改进后的灰狼优化(IMOGWO)算法进行无人机蜂群任务分配。仿真验证结果表明,IMOGWO算法具有更高的有效性、充分性和收敛性,获取最佳任务分配方案的用时更少,任务分配方案更优,优化后的算法基本满足无人机蜂群的任务分配要求。
In order to improve the task execution efficiency of UAV swarms and the probability of winning the battle between UAV swarms and enemy,multi-population mechanism and Pareto allocation theory are introduced to improve the multi-objective gray wolf optimization algorithm,and the improved gray wolf optimization(MP-MOGWO)algorithm is used for UAV swarm task assignment.The simulation results show that the MP-MOGWO algorithm has better task assignment scheme,and has higher effectiveness,adequacy and convergence.In addition,it takes less time to obtain the best task assignment scheme,and the task assignment scheme is better.The optimized algorithm basically meets the task assignment requirements of UAV swarms.
作者
司翠平
刘映泉
Si Cuiping;Liu Yingquan(Department of Electronic Engineering,Nanjing Vocational Institute of Mechatronic Technology,Jiangsu Nanjing,211135,China;Nanjing Dwing Aviation Technology Co.,Ltd.,Jiangsu Nanjing,211800,China)
出处
《机械设计与制造工程》
2023年第9期45-49,共5页
Machine Design and Manufacturing Engineering
基金
2020年江苏省高职院校青年教师企业实践项目(2020QYSJ160)。
关键词
无人机蜂群
任务分配
灰狼算法
多种群机制
unmanned aerial vehicle swarms
task assignment
gray wolf algorithm
multi-population mechanism
