摘要
针对舰载机机群着舰回收排序调度问题,首先对航母甲板环境以及舰载机的返航回收进场模式进行了分析,建立了基于加权等待时间的回收排序评价指标模型,并根据舰载机回收着舰排序调度问题中的各种约束,建立了考虑空中加油条件的舰载机回收排序调度问题模型。然后,根据所建立的回收调度排序模型以及超启发式算法的思想,设计了一种带强制着舰规则的遗传规划(genetic programming with mandatory landing rules,MGP)算法,用于对着舰回收排序调度问题进行求解。进一步,借助仿真算例验证了所建回收排序调度模型和MGP算法的有效性,并通过与遗传算法、纯启发式算法以及普通的遗传规划算法进行对比,验证了MGP算法的优势。最后,基于算例仿真结果,分析了逃逸复飞对着舰回收方案的影响。
Aiming at the problem of the recovery sequencing and scheduling of the carrier aircraft fleet landing,the carrier deck environment and the return recovery approach mode of the carrier aircraft are analyzed firstly,and the evaluation index model of the recovery sequencing and scheduling based on the weighted waiting time is established.According to the various constraints in the recovery sequencing and scheduling problem of the carrier aircraft,the model of the recovery sequencing and scheduling problem of the carrier aircraft considering the air refueling condition is established.Then,according to the established recycling sequencing and scheduling model and the idea of huper-heuristic algorithm,a genetic programming with mandatory landing rules(MGP)algorithm is designed to solve the ship necovery scheduling problem.Further,the effectiveness of the proposed recovery sequencing and scheduling model and MGP algorithm is verified by simulation examples,and the advantages of MGP algorithm are verified by comparison with genetic algorithm,pure heuristic algorithm and common genetic programming algorithm.Finally,based on the simulation results of an example,the influence of escape and wave off on the landing fleet recovery scheme is analyzed.
作者
崔凯凯
崔荣伟
韩维
郭放
王毓麟
刘洁
CUI Kaikai;CUI Rongwei;HAN Wei;GUO Fang;WANG Yulin;LIU Jie(School of Basic Sciences for Aviation,Naval Aviation University,Yantai 264001,China;Unit 92942 of the PLA,Beijing 100161,China;War Research Institute,Academy of Military Sciences,Beijing 100850,China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2023年第10期3192-3206,共15页
Systems Engineering and Electronics
关键词
舰载机
回收排序
空中加油
超启发算法
遗传规划
carrier aircraft
recovery sequencing
air refueling
hyper-heuristic algorithm
genetic programming
