摘要
在多基地多目标多无人飞行器(unmanned aerial vehicle,UAV)的协同任务规划这类约束条件众多、复杂且耦合的多目标优化与决策问题中,利用传统的粒子群优化算法在寻优时容易陷入局部最优,为此,提出了一种基于模拟退火的混合粒子群算法。基于攻打任务背景,综合考虑无人机的物理性能约束,搭建航迹长度最小适应度函数和威胁代价最小适应度函数以构造目标函数,先利用Voronoi图以及Dijkstra算法进行航迹规划,再利用基于模拟退火的混合粒子群算法进行任务分配。仿真结果表明:所提算法融合了模拟退火算法、粒子群优化算法的优点,能快速求解UAV任务规划的近似最优解,且与粒子群优化算法和模拟退火算法相比,在进化次数足够多的情况下该方法得到的结果更优。
In the multi-objective optimization and decision-making problem of multi-objective and multi-unmanned aerial vehicle(UAV),which has many constraints,complex and coupling,the traditional particle swarm optimization(PSO)algorithm is easy to fall into the local optimal in the optimization.Therefore,a hybrid particle swarm optimization algorithm based on simulated annealing(SA-PSO)is proposed.Based on the background of the attack task,comprehensively considers the physical performance constraints of the UAV,builds the minimum fitness function of the track length and the minimum fitness function of the threat cost to construct the objective function.First,use the Voronoi diagram and the Dijkstra algorithm for path planning,then a hybrid particle swarm optimization algorithm based on simulated annealing is used for task assignment.The simulation results show that the proposed algorithm combines the advantages of simulated annealing algorithm(SA)and particle PSO,which can quickly solve the approximate optimal solution of UAV task planning and is compatible with PSO algorithm.Compared with the SA algorithm,better results is obtained when there are enough evolution times.
作者
潘楠
刘海石
陈启用
颜礼贤
郭晓珏
PAN Nan;LIU Hai-shi;CHEN Qi-yong;YAN Li-xian;GUO Xiao-jue(Kunming University of Science&Technology,Faculty of Civil Aviation and Aeronautical,Yunnan Kunming 650500,China;Kunming University of Science&Technology,Faculty of Materials Science and Engineering,Yunnan Kunming 650500,China;Kunming Zhiyuan Measurement and Control Technology Co.,Ltd.,Yunnan Kunming 650500,China)
出处
《现代防御技术》
北大核心
2021年第2期49-56,共8页
Modern Defence Technology
关键词
无人飞行器
攻打
任务规划
迪杰斯特拉算法
模拟退火算法
粒子群算法
unmanned aerial vehicle(UAV)
attack
mission planning
Dijkstra algorithm
simulated annealing(SA)algorithm
particle swarm optimization(PSO)algorithm
