基于多目标MQABC算法的无人机协同任务分配  

UAV cooperative task allocation based on multi-objective MQABC algorithm

作　　者：赵辉[1] 李牧东[1] 韩统[1] 黄汉桥[1] 

机构地区：[1]空军工程大学航空航天工程学院,西安710038

出　　处：《华中科技大学学报（自然科学版）》2016年第3期121-126,共6页Journal of Huazhong University of Science and Technology(Natural Science Edition)

基　　金：中国航空科学基金资助项目(20105196016);中国博士后科学基金资助项目(2012M521807)

摘　　要：针对多无人机协同任务分配问题经过单目标简化后对决策处理存在片面性和主观性等问题,提出了一种利用多目标自适应快速人工蜂群算法对其进行处理的方法.首先,建立多目标无人机协同任务分配模型;其次通过建立外部种群的约束处理技术及重置Harmonic平均距离循环策略对自适应快速人工蜂群算法(ABCSGQ)进行改进.另外通过定义自主决策准则引导多目标任务分配的方案选取.仿真实验结果表明:相比于多目标人工蜂群算法及非支配排序遗传算法,改进算法具有较好的分布性、收敛性及更高效的求解能力.Unmanned aerial vehicle（UAV）cooperative task allocation model simplified by single objective function has the insufficient of one-sidedness and subjectivity for task decision.The multi-objective self-adaptive quick artificial bee colony（MQABC）algorithm was proposed to solve the problem.Firstly,the multi-objective（UAV）cooperative task allocation model was established.Secondly,the artificial bee colony with self-adaptive global best-guided quick search strategy（ABCSGQ）was improved by the external population constraint processing technology and reset Harmonic mean distance cycle strategy.In addition,the self-decision rule was introduced to guide the decision-maker to make a better decision.Simulation results demonstrates that MQABC has a better performance in distribution,convergence and multi-objective problem solving skill compared with multi-objective artificial bee colony（MABC）and nondominated sorting genetic algorithm II（NSGA-II）.

关 键 词：无人机 多目标优化 协同任务分配 人工蜂群算法 快速收敛 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]