摘要 针对多无人机对空中移动目标协同执行多任务问题,本文提出了一种基于并行机制的多目标灰狼优化算法。结合无人机空中态势模型,以最小化执行代价和最小化时间代价为双目标函数,建立了多无人机协同多任务分配模型;将多个无人机视为并行的灰狼子群,对每个子群分别采用分层编码和多目标优化算法保留其最优个体;通过档案室共享策略获得整个群体的最优解;仿真对比验证了改进多目标灰狼优化算法与传统的智能算法。研究结果表明:与多目标灰狼优化算法和多目标粒子群算法相比,基于并行机制的多目标灰狼优化算法在代价函数均值方面分别降低了约3.8%和4.1%,在收敛值方面分别降低了约15.5%和6.2%,具有更好的稳定性和收敛性。 This paper presents an improved multi objective grey wolf optimization algorithm based on parallel mechanism for multiple unmanned aerial vehicles(UAVs)performing multiple tasks in coordination with multiple aerial moving targets.Firstly,a cooperative multiple task assignment model for multi UAV is built by introducing the aerial situation model,with carry cost and time cost as a dual objective function.Secondly,the multi UAV team is regarded as several parallel sub swarms,and the layered encoding and multi objective optimization strategies are adopted to preserve the optimal individuals of each sub swarm simultaneously.Finally,the archive shared strategy is used to obtain the optimal solution of the whole swarm.The performance of the proposed algorithm is verified by simulation experiments.The results show that compared with the multi objective grey wolf optimization and multi objective particle swarm optimization,the proposed algorithm’s average costs drop by about 3.8%and 4.1%,and its convergence values drop by about 15.5%and 6.2%,indicating that this new method has better stability and convergence performance.
出处 《西安工业大学学报》 CAS 2021年第1期94-102,共9页 Journal of Xi’an Technological University
基金 陕西省2020年重点研发计划(2020GY 073)。
关键词 任务分配 多目标灰狼优化算法 分层编码 档案室共享策略 协同多任务分配模型 task assignment multi objective grey wolf optimization algorithm layered encoding archive shared strategy cooperative multiple task assignment problem
