基于AHP-DQN的作战效能优化方法研究  

Research on optimization method of combat effectiveness based on AHP-DQN

作　　者：王国岩 曹红松[1] 刘鹏飞[1] 张芝源 翟超凡 WANG Guoyan;CAO Hongsong;LIU Pengfei;ZHANG Zhiyuan;ZHAI Chaofan(College of Mechatronic Engineering,North University of China,Taiyuan 030051,China)

机构地区：[1]中北大学机电工程工程学院,山西太原030051

出　　处：《指挥控制与仿真》2023年第3期78-86,共9页Command Control & Simulation

摘　　要：针对舰艇编队威胁海域安全的案例,设计了红方突防攻击、蓝方防空反导的想定以及陆、海、空3种反航母作战策略,基于墨子系统开展多方案作战仿真,获得了双方战损数据。在此基础上,建立了以引诱、攻击等性能参数为基础的指标体系,以层次分析法(AHP)分析了3种策略的作战效能。进一步提出了一种以AHP权重定奖励的深度强化学习(DQN)算法,对海基策略进行了优化,作战效能提高了5.36%。研究结果表明,基于墨子系统这一类作战仿真软件开展想定设计、作战策略仿真,再建立AHP-DQN进行作战效能优化的方法可为反舰作战提供参考。In view of the case that the fleet of ships threatens the safety of the sea area,the scenarios of red side penetration attack,blue side air defense and antimissile,as well as three kinds of anti aircraft carrier combat strategies of land,sea and air are designed.Based on the Mozi system,the multi scheme combat simulation is carried out,and the battle damage data of both sides are obtained.On this basis,an index system based on the performance parameters of decoy and attack is established,and the operational effectiveness of the three strategies is analyzed with the analytic hierarchy process(AHP).Furthermore,a Deep Reinforcement Learning(DRL)algorithm based on AHP weight is proposed,which optimizes the sea based strategy and improves the combat effectiveness by 5.36%.The research results show that the method of scenario design,combat strategy simulation,and AHP-DQN for operational efficiency optimization based on such combat simulation software as Mozi system can provide reference for anti-ship warfare.

关 键 词：作战推演 打击策略 层次分析法 深度强化学习 效能优化 

分 类 号：TJ76[兵器科学与技术—武器系统与运用工程] E834[军事—战术学] TP391.9[自动化与计算机技术—计算机应用技术]