基于自适应遗传学习粒子群算法的多无人机协同任务分配  

Adaptive genetic learning particle swarm optimization based cooperative task allocation for multi-UAVs

作　　者：张祥银 夏爽[1,2] 张天 ZHANG Xiang-yin;XIA Shuang;ZHANG Tian(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China;Engineering Research Centre of Digital Community of Ministry of Education,Beijing 100124,China;Beijing Institute of Artificial Intelligence,Beijing University of Technology,Beijing 100124,China)

机构地区：[1]北京工业大学信息学部,北京100124 [2]数字社区教育部工程中心,北京100124 [3]北京工业大学北京人工智能研究院,北京100124

出　　处：《控制与决策》2023年第11期3103-3111,共9页Control and Decision

基　　金：国家自然科学基金项目(61703012);北京市自然科学基金项目(4182010)。

摘　　要：研究救援场景下的多无人机协同任务分配问题,考虑幸存者所需援助类型的不同,建立更贴合实际的组合优化模型.针对该模型,提出一种自适应遗传学习粒子群算法(adaptive genetic learning particle swarm optimization,AGLPSO).首先,根据无人机与幸存者之间的救援关系,采用一种实向量编码机制处理决策变量约束,以简化模型求解.然后,通过两层级联结构提高算法搜索能力:第1层通过遗传学习策略生成高质量的精英粒子,并对进化停滞的粒子采用精英学习策略进行更新,以跳出局部最优;第2层利用精英粒子指导种群的搜索方向,并根据粒子群的进化速度和粒子的聚集程度,采用自适应进化策略提高算法在不同进化时期的寻优能力.仿真实验表明,所提出的AGLPSO算法能快速、有效地找到合理的救援分配方案.The problem of cooperative task allocation for multi-UAVs in rescue scenarios is studied.Considering the different types of assistance required by survivors,a more practical combinatorial optimization model is established,and an adaptive genetic learning particle swarm optimization(AGLPSO)algorithm is proposed for this model.Firstly,according to the rescue relationship between UAVs and survivors,a real vector coding mechanism is adopted to deal with the constraints of decision variables to simplify the solution of the model.Then,the search ability of the algorithm is improved through the two cascading layers.In the first layer,the genetic learning strategy is used to generate elite particles with high quality,and the evolutionary stagnation particles are updated by the elite learning strategy to jump out of local optimum.In the second layer,the search direction of the population is guided by the elite particles,and according to the evolution speed of particle swarm and aggregation degree of particles,the adaptive evolution strategy is used to improve the searching ability of the algorithm in different evolutionary periods.The simulation results show that the proposed AGLPSO algorithm can quickly and effectively find a reasonable rescue allocation scheme.

关 键 词：协同任务分配 粒子群算法 实向量编码 遗传学习 精英学习 自适应 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]