复杂约束条件下的UCAV多重任务分配

Multiple Task Assignment of Unmanned Combat Aerial Vehicles Under Complex Constraints

与单任务分配问题相比,无人作战飞机(UCAV)多重任务分配具有更复杂的约束条件.基于UCAV任务分配有向图给出了多重任务分配的一般框架,分析了任务分配的约束条件,提出了一种求解约束优化问题的改进遗传算法.其基本思想是对种群中的个体按两种方案评价,对可行解按目标函数值大小,对不可行解按约束违反程度进行评价,避免了罚函数法中的罚因子的选取难题.采用矩阵形式进行个体编码,按目标出现顺序映射任务类型,解决了多重任务的编码表示,并对武器类型向量实施绑定策略,简化了问题复杂性.设计了选择,交叉,变异等遗传操作算子,保证生成的新染色体不破坏编码时满足的约束条件.最后进行了仿真试验,结果表明提出的方法求解UCAV多重任务分配问题的可行性和有效性.

Multiple task assignment about Unmanned Combat Aerial Vehicles（UCAV） has more complex constraints compared to the si^tgle one. This paper presents a framework of UCAV task assignment based on the task assignment direction graph, and discusses some constraints with task assignment. The basic concept is to evaluate the feasible and infeasible solutions respectively. It evaluates the feasible solutions according to the value of the objective function and the infeasible ones according to the degree of the violation of the constraints. The individual coding is showed by the matrix and the multiple task is solved under the help of target orders. In order to reduce the problem complexity, the weapon types are bind to the targets. Then this paper designs special selection, crossover, inverse and mutation operations, ensures the new chromosomes meet the same constraints as the coding. At last, experiments are made and simulation results show the feasibility and effectiveness of this approach to resolve complex task assignment.