不确定条件下基于烟花算法的无人机任务分配  

UAV task allocation based on firework algorithm under uncertain conditions

作　　者：余稼洋 郭建胜[1] 张晓丰[1] 解涛 姚赛 YU Jiayang;GUO Jiansheng;ZHANG Xiaofeng;XIE Tao;YAO Sai(College of Equipment Management and Unmanned Aerial Vehicle Engineering,Air Force Engineering University,Xi’an 710051,China)

机构地区：[1]空军工程大学装备管理与无人机工程学院,西安710051

出　　处：《兵器装备工程学报》2023年第4期104-111,共8页Journal of Ordnance Equipment Engineering

摘　　要：无人机任务分配问题是近几年的研究热点,但同时考虑不确定性和多目标的研究尚处于起步阶段。基于不确定性理论,建立了一种目标函数和约束条件均包含不确定变量的不确定多目标任务分配模型,并分别引入期望值准则和机会约束将其转化为确定型优化模型。针对传统烟花算法只能解决单目标问题和在收敛速度方面的不足,引入幂律分布函数和Levy变异算子,结合多目标优化理论和两阶段搜索策略设计了一种两阶段搜索的多目标烟花算法。通过实例仿真验证所提模型及算法的可行性和有效性。UAV task allocation is a hot topic in recent years,while the research considering both uncertainty and multi-objective development is still in its infancy.Firstly,based on the uncertainty theory,this paper establishes a mathematical model of uncertain multi-objective task allocation with uncertain variables included in objective functions and constraint conditions,and introduces the expected value criterion and opportunity constraint to transform the model into an optimized deterministic one.Furthermore,in order to solve the problem of dealing with single objective problems only and overcome the shortcoming of the convergence speed of the traditional firework algorithm,a multi-objective firework algorithm with two-stage search is designed by introducing power-law distribution function and Levy variation operator,combined with multi-objective optimization theory and two-stage search strategy.Finally,the feasibility and effectiveness of the proposed model and algorithm are verified by example simulation.

关 键 词：不确定理论 无人机多目标任务分配 烟花算法 幂律分布 Levy变异 两阶段搜索 

分 类 号：V19[航空宇航科学与技术—人机与环境工程]