摘要
多无人机任务分配是复杂的多约束多目标非线性优化问题,针对常用智能算法的各自缺陷,结合烟花算法特点,提出使用改进的自适应烟花算法处理该问题。首先建立了该问题的数学模型,使用层次分析法结合灰色关联法求得各指标权重,并用外罚函数法将该模型转化为无约束单目标极值问题;然后使用自适应烟花算法求解该模型。为验证自适应烟花算法处理该问题的优越性,分别用几种不同优化算法做仿真计算。结果表明,自适应烟花算法能快速收敛于全局最优解,其结果直观地表述了该复杂情景下合理任务分配方案。
The task allocation for multiple UAVs is a complex multi-constraint, multi-objective, nonlinear optimization problem. Aiming at the defect of each commonly-used intelligent algorithm, and based on the characteristics of fireworks algorithm, this paper proposes an improved adaptive fireworks algorithm to deal with the problem. The mathematical model of the problem is built firstly, then, the weight of each index is obtained by using AHP together with the gray correlation method. The model is then transformed into a problem of unconstrained, single-objective extremum value by using the external penalty function method. After that, the adaptive fireworks algorithm is used to solve the model. In order to verify the superiority of adaptive fireworks algorithm in dealing with this problem, several different optimization algorithms are used for simulation. The experimental simulation results show that, the adaptive fireworks algorithm can quickly converge to the global optimum solution, and the result intuitively describes the reasonable task allocation scheme under complex situations.
出处
《电光与控制》
北大核心
2018年第1期37-43,共7页
Electronics Optics & Control
关键词
协同任务分配
外罚函数法
烟花算法
多约束多目标优化
cooperative task assignment
external penalty function method
fireworks algorithm
multi- constrained multi-objective optimization
