控制理论与应用2024，Vol.41Issue(6)：1009-1017,9.DOI:10.7641/CTA.2023.20665
LeCMPSO算法求解异构无人机协同多任务重分配问题
Learning-guided coevolution multi-objective particle swarm optimization for heterogeneous UAV cooperative multi-task reallocation problem
摘要
Abstract
UAV system has been widely used in military field.Due to the complex and changeable battlefield environ-ment,UAV tasks need to be reassigned after an emergency.Heterogeneous UAVs refer to multiple types of UAVs,which can accomplish multiple types of complex tasks that a single UAV can not.The heterogeneous UAV cooperative multi-task reallocation problem has complex constraints and mixed variables,and the existing multi-objective optimization algorithms can not deal with this kind of problems effectively.In order to solve the above problems efficiently,a multi-constraint heterogeneous UAVs cooperative multi-task reallocation model is constructed at first in this paper,and a learning-guided cooperative multi-objective particle swarm optimization algorithm(LeCMPSO)is proposed to solve that.In LeCMPSO,a prior knowledge based initialization strategy as well as a history information learning based particle update strategy are introduced to avoid the generation of infeasible solutions and improve the search efficiency of the algorithm.The simulation results on 4 sets of examples show that the proposed algorithm outperforms the other typical coevolutionary multi-objective optimization algorithms on diversity of solution sets,convergence,and search time.关键词
无人机多任务重分配/粒子群优化算法/多目标优化/协同进化Key words
UAV multi-task reallocation/particle swarm optimization/multi-objective optimization/coevolution引用本文复制引用
王峰,付青坡,韩孟臣,邢立宁,吴虎胜..LeCMPSO算法求解异构无人机协同多任务重分配问题[J].控制理论与应用,2024,41(6):1009-1017,9.基金项目
国家自然科学基金项目(62173258)资助.Supported by the National Natural Science Foundation of China(62173258). （62173258）
