摘要
无人水下航行器(UUV)集群将成为未来水下作战的重要力量, UUV集群的打击任务分配问题是UUV运用过程中的关键问题之一,可以看作为一个武器-目标分配问题,也是一个多约束多目标优化问题。因此,考虑UUV对敌舰探测概率、载弹量、弹药成本、杀伤概率和目标价值,以及UUV对鱼雷的拦截概率等因素,建立打击收益和弹药消耗成本2个目标函数,构建了UUV集群打击任务分配模型。引入多目标优化算法——第3代非支配排序遗传算法(NSGA-Ⅲ)对该模型进行求解,分析了编码策略、约束处理方法以及NSGA-Ⅲ的流程和关键步骤。仿真结果表明,与NSGA-Ⅱ、自适应形状估计多目标进化算法(AGEMOEA)相比,该算法在运行时间和反世代距离2项指标上表现更好,能够更好地为决策提供支撑。
The unmanned undersea vehicle(UUV)cluster will become important in future underwater operations.The taskallocation of the UUV cluster is a key problem in the application of the UUV cluster,and it can be regarded as a weapon–targetassignment problem and a multiconstraint and multiobjective optimization problem.Therefore,considering factors such as thedetection probability,ammunition load,ammunition cost,kill probability,target value of enemy ships,and interceptionprobabilities of torpedoes around the UUV,two objective functions for the attack income and ammunition consumption costare established,and a UUV cluster strike task allocation model is constructed.A multiobjective optimization algorithm,i.e.,the non-dominated sorting genetic algorithm-III(NSGA-III),is introduced to solve the model.The coding strategy,constraintprocessing method,process,and key steps of NSGA-III are analyzed.Simulation results indicate that this algorithmoutperforms NSGA-II and AGE-multi-objective evolutionary algorithms(AGE-MOEA)in terms of the running time andinverted generational distance and thus can support effective decision making.
作者
吴思聪
吴曦
WU Sicong;WU Xi(School of Joint Operation,National Defense University,Beijing,100091;Graduate College,National Defense University,Beijing,100091)
出处
《水下无人系统学报》
2023年第3期474-480,共7页
Journal of Unmanned Undersea Systems
关键词
无人水下航行器
武器-目标分配
任务分配
多目标优化
非支配排序遗传算法
unmanned undersea vehicle
weapon-target assignment
task allocation
multi-objective optimization
non-dominated sorting genetic algorithm
作者简介
吴思聪(1995-),男,在读硕士,主要研究方向为联合作战体系仿真.
