基于改进的多目标量子行为粒子群优化算法的多无人机协同任务分配  

Cooperative Task Allocation for Multiple UAVs Based on Improved Multi-objective Quantum-behaved Particle Swarm Optimization Algorithm

作　　者：施展[1,2] 陈庆伟[1] 

机构地区：[1]南京理工大学自动化学院,江苏南京210094 [2]中国电子科技集团公司第二十八研究所,江苏南京210007

出　　处：《南京理工大学学报》2012年第6期945-951,共7页Journal of Nanjing University of Science and Technology

基　　金：国家自然科学基金(60975075;61074023);总装'十二五'预先研究项目;江苏省自然科学基金(BK2008404)

摘　　要：为在给定的时间内以最小代价和最大效益完成任务,建立了多无人机协同任务分配问题的多目标优化模型。采用改进的多目标量子行为粒子群优化算法求解最优任务分配方案,定义了一种从所求候选方案中选取最优分配方案的自主选择准则。对比分析多目标粒子群优化、多目标进化算法和该文算法所求的最优分配方案。仿真结果表明该文算法能够较快地求解问题,而且所求最优任务分配方案的性能优于其它三种算法。To accomplish ordered missions with the least cost and most benefit in given time,a multi- objective optimization model for cooperative task allocation of multiple unmanned aerial vehicles （UAVs） is established. An improved multi-objective quantum-behaved particle swarm optimization algorithm is used to solve the optimal task allocation scheme. An autonomous selection criterion for optimal allocation scheme choice in the obtained optional schemes is defined. The optimal allocation schemes solved by the multi-objective particle swarm optimization, the multi-objective evolutionary algorithm and the algorithm in this paper are contrastly analyzed. The simulation results show the proposed approach can solute the problem fast, and the performance of the task allocation scheme of this approach is better than others.

关 键 词：多无人机 多目标优化 量子行为粒子群优化 任务分配 自主选择 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] V279[自动化与计算机技术—计算机科学与技术]