基于文化基因算法求解动态武器目标分配  

Dynamic Weapon Target Assignment Based on Cultural Gene Algorithm

作　　者：强裕功 宋贵宝 刘铁 贺洁 陈天柱 Qiang Yugong;Song Guibao;Liu Tie;He Jie;Chen Tianzhu(College of Coast Guard,Naval Aviation University,Yantai 264001,China;No.92132 Unit of PLA,Qingdao 266000,China;No.91913 Unit of PLA,Huludao 125000,China)

机构地区：[1]海军航空大学岸防兵学院,山东烟台264001 [2]中国人民解放军92132部队,山东青岛266000 [3]中国人民解放军91913部队,辽宁葫芦岛125000

出　　处：《兵工自动化》2024年第4期7-13,共7页Ordnance Industry Automation

摘　　要：针对动态武器目标分配(dynamic weapon target assignment,DWTA)问题,提出一种基于进化算法和局部搜索算法的文化基因算法(memetic algorithm,MA)。以最大化目标毁伤为目标,建立考虑能力约束、策略约束、资源约束、拦截可行性约束条件下的DWTA模型;引入虚拟排列进行编码以满足拦截可行性要求,设计将排列转化为实际分配方案的构造方法,给出算法运行过程中对随机事件的处理方法。通过与遗传算法(genetic algorithm,GA)、MA-GLS(memetic algorithm global local search)求解不同测试实例的对比仿真,结果表明,MA算法具有寻优速度快、优化能力强、稳定性好的优点。For the dynamic weapon target assignment(DWTA)problem,a memetic algorithm(MA)based on evolutionary algorithm and local search algorithm is proposed.In order to maximize the target damage,the DWTA model considering the constraints of capacity,strategy,resource and interception feasibility was established.A virtual permutation was introduced to encode the DWTA model to meet the requirement of interception feasibility,and the construction method of transforming the permutation into the actual allocation scheme was designed,and the method of dealing with random events in the operation process of the algorithm was given.Compared with genetic algorithm(GA)and memetic algorithm global local search(MA-GLS)in solving different test cases,the simulation results show that MA algorithm has the advantages of fast optimization speed,strong optimization ability and good stability.

关 键 词：动态目标分配 文化基因算法 随机事件 

分 类 号：TJ761.1[兵器科学与技术—武器系统与运用工程] E273.4[军事—军事理论]