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WANG Zhaochen, YANG Huadong, SUN Haiwen, JIN Zirong. Research Status and Development of Intelligent Optimization Methods for Mission Schemes[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0066
Citation: WANG Zhaochen, YANG Huadong, SUN Haiwen, JIN Zirong. Research Status and Development of Intelligent Optimization Methods for Mission Schemes[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0066

Research Status and Development of Intelligent Optimization Methods for Mission Schemes

doi: 10.11993/j.issn.2096-3920.2025-0066
  • Received Date: 2025-05-16
  • Accepted Date: 2025-07-03
  • Rev Recd Date: 2025-06-12
  • Available Online: 2025-12-09
  • The mission environment has become more and more complex, and the tempo has obviously accelerated. As a result, the traditional manual decision-making can no longer meet the requirements. There is a strong need for an advanced decision-making system to assist decision makers in carrying out on-the-spot mission command. To better carry out the research on intelligent recommendation methods for mission schemes, this paper collated the research articles in this direction in China and abroad in recent years and divided the intelligent recommendation methods into three categories, namely intelligent analysis, intelligent matching, and intelligent learning. It elaborated on the core principles, technical paths, and typical applications of various methods and simultaneously analyzed the advantages and disadvantages of the three types of methods. It identified the deficiencies of the existing methods in terms of dynamic adaptability, autonomous decision-making ability, data dependence, and credibility. Finally, the future development direction was prospected, providing valuable references for subsequent research in this field.

     

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  • [1]
    焦鹏博, 罗志浩, 范长俊, 等. 作战方案智能推荐方法综述[J/OL]. 系统工程与电子技术, 1-17[2025-11-12]. https://link.cnki.net/urlid/11.2422.TN.20241010.0913.002

    JIAO P B, LUO Z H, FAN C J, et al. Review on intelligent recommendation methods for combat plans[J]. Systems Engineering and Electronics: 1-17[2025-11-12]. https://link.cnki.net/urlid/11.2422.TN.20241010.0913.002
    [2]
    陈东林, 赵利佳, 赵岳, 等. 基于深度神经网络的作战方案辅助生成研究[C]//第十届中国指挥控制大会论文集(上册). 北京, 中国: 中国指挥与控制学会, 2022: 249-253.
    [3]
    GIN C R, SHEA D E, BRUNTON S L, et al. DeepGreen: Deep learning of Green’s functions for non-linear boundary value problems[J]. Scientific Reports, 2021, 11(1): 21614. doi: 10.1038/s41598-021-00773-x
    [4]
    李皓, 常国岑, 孙鹏, 等. 基于Agent的作战方案自动生成系统研究[J]. 系统工程与电子技术, 2009, 31(1): 134-136.

    LI H, CHANG G C, SUN P, et al. Research on agent-based automatic generation system for combat plans[J]. Systems Engineering and Electronics, 2009, 31(1): 134-136.
    [5]
    徐任杰, 宫琳, 朱明仁, 等. 不确定信息下考虑相关性与多样性的作战方案推荐方法[J]. 系统工程与电子技术, 2022, 44(10): 3115-3123.

    XU R J, GONG L, ZHU M R, et al. A method for recommending combat plans considering correlation and diversity under uncertain information[J]. Systems Engineering and Electronics, 2022, 44(10): 3115-3123.
    [6]
    ZHAO Y, DU K. A matching scheme from supply and demand sides of electronic health records based on blockchain[C]//2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP). Xi’an, China: IEEE, 2022: 1089-1092.
    [7]
    QU Z T, HE P. Interactive intelligent analysis method: An application of criminal investigation[C]//2009 International Symposium on Intelligent Ubiquitous Computing and Education. Chengdu, China: IEEE, 2009: 152-155.
    [8]
    谢炜, 黄建业, 程斌, 等. 基于大语言模型的知识图谱复杂逻辑推理方法[C]//第39次全国计算机安全学术交流会论文集. 西安, 中国: 中国计算机学会, 2024: 175-180.
    [9]
    HARB H, NADER D, SABEH K, et al. Real-time approach for decision making in IoT-based applications[C]//International Conference on Sensor Networks. On Line: HAL, 2022: 223-230.
    [10]
    徐涛. 基于知识图谱的数据关联融合技术研究[J]. 软件, 2024, 45(10): 96-98.

    XU T. Research on data association and fusion technology based on knowledge graph[J]. Software, 2024, 45(10): 96-98.
    [11]
    邱天搏, 张东, 李冠宇, 等. MSIM: 融合注意力机制的多阶段推理知识图谱问答模型[J]. 计算机工程与应用, 2024: 1-12.

    Qiu Tianbo, Zhang Dong, Li Guanyu, et al. MSMI: A multi-stage reasoning knowledge graph question an-swering model with attention mechanism[J]. Computer Engineering and Applications, 2024: 1-12.
    [12]
    刘将. 基于深度学习的知识图谱关系推理算法优化及系统实现[D]. 北京: 北京邮电大学, 2023
    [13]
    GENG D Q, DENG J. Knowledge graph embedding model based on multi-hop adaptive graph attention net-work[C]// 2024 36th Chinese Control and Decision Conference (CCDC). Xi’an, China: IEEE, 2024: 3086-3091.
    [14]
    殷泽恒, 余敦辉, 邓怡辰, 等. 融合逻辑规则和推理路径嵌入的知识图谱推理[J]. 微电子学与计算机, 2025, 42(9): 134-144. doi: 10.19304/J.ISSN1000-7180.2024.0640

    YIN Z H, YU D H, DENG Y C, et al. Knowledge graph reasoning based on the integration of logical rules and embedding of inference paths[J]. Microelectronics and Computer, 2025, 42(9): 134-144. doi: 10.19304/J.ISSN1000-7180.2024.0640
    [15]
    吴冰涛. 基于扩展置信规则库的规则推理网络模型[D]. 石家庄: 石家庄铁道大学, 2024
    [16]
    宋晨烨, 贺筱媛, 郭圣明, 等. 基于时序知识图谱的智能任务推断方法[J]. 系统仿真技术, 2024, 20(3): 275-281,306.

    SONG C Y, HE X Y, GUO S M, et al. Intelligent task inference method based on temporal knowledge graph[J]. System Simulation Technology, 2024, 20(3): 275-281,306.
    [17]
    胡诗, 毛杰. 海上编队协同作战规则推理技术研究[J]. 舰船电子工程, 2017, 37(5): 109-113.

    HU S, MAO J. Research on reasoning technology for rules of sea battle group’s cooperative operations[J]. Ship Electronic Engineering, 2017, 37(5): 109-113.
    [18]
    SAID A M, MAROT M, BOUCETTA C, et al. Reinforcement learning vs rule-based dynamic movement strategies in UAV assisted networks[R]. Vehicular Communications, 2024, 48: 100788.
    [19]
    胡诗. 作战方案全要素仿真推演技术研究[J]. 舰船电子工程, 2019, 39(12): 11-17.

    HU S. Research on full-element simulation and drift evaluation technology for combat plans[J]. Ship Electronic Engineering, 2019, 39(12): 11-17.
    [20]
    YANG Z L, BONSALL S, WANG J. Fuzzy rule-based bayesian reasoning approach for prioritization of failures in FMEA[J]. IEEE Transactions on Reliability, 2008, 57(3): 517-528. doi: 10.1109/TR.2008.928208
    [21]
    Research and Solution Analysis on Key Technologies of Intelligent Distribution Network and New Energy Grid Connection[J]. Chongqing VIP Information Co. , Ltd. , 2022.
    [22]
    周攀, 黄江涛, 章胜, 等. 基于深度强化学习的智能空战决策与仿真[J]. 航空学报, 2023, 44(4): 99-112.

    ZHOU P, HUANG J T, ZHANG S, et al. Intelligent air combat decision-making and simulation based on deep reinforcement learning[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(4): 99-112.
    [23]
    邱少明, 王雪珂, 杜秀丽, 等. 基于优化BP神经网络的气象环境下军事通信效能评估[J]. 火力与指挥控制, 2022, 47(3): 89-96.

    QIU S M, WANG X K, DU X L, et al. Evaluation of military communication effectiveness under meteorological environments based on optimized BP neural network[J]. Firepower and Command and Control, 2022, 47(3): 89-96.
    [24]
    陈强, 陈长兴, 陈婷, 等. 基于灰色层次分析法-BP神经网络的数据链系统效能评估[J]. 弹箭与制导学报, 2016, 36(3): 109-113,116.

    CHEN Q, CHEN C X, CHEN T, et al. Data link system effectiveness evaluation based on grey analytic hierarchy process - BP neural network[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2016, 36(3): 109-113,116.
    [25]
    唐永果. 基于APSO-BP神经网络的末敏弹作战效能评估方法[J]. 兵器装备工程学报, 2024, 45(10): 100-106.

    TANG Y G. Evaluation method for the operational effectiveness of terminal munitions based on APSO-BP neural network[J]. Journal of Armament and Equipment Engineering, 2024, 45(10): 100-106.
    [26]
    勾起跃, 邓满琪, 呼凯凯, 等. 基于灰色-粗糙集的雷达阵地工程建设风险评价指标体系构建[J]. 项目管理技术, 2024, 22(11): 102-107.

    GOU Q Y, DENG M Q, HU K K, et al. Construction risk evaluation index system for radar positioning facilities based on grey-rough set[J]. Project Management Technology, 2024, 22(11): 102-107.
    [27]
    韩斌, 苏奎峰. 改进型RBF神经网络下目标威胁评估[J]. 价值工程, 2015, 34(6): 306-307. doi: 10.14018/j.cnki.cn13-1085/n.2015.06.170

    HAN B, SU K F. Target threat assessment based on improved RBF neural network[J]. Engineering Economics, 2015, 34(6): 306-307. doi: 10.14018/j.cnki.cn13-1085/n.2015.06.170
    [28]
    冯卉, 宋宝军, 邢清华, 等. 基于直觉模糊VIKOR决策的反导作战预案评估方法[J]. 火力与指挥控制, 2022, 47(6): 17-21,27.

    FENG H, SONG B J, XING Q H, et al. Evaluation method for anti-missile combat plans based on intuitionistic fuzzy VIKOR decision-making[J]. Firepower and Command and Control, 2022, 47(6): 17-21,27.
    [29]
    欧一鸣, 苏雍贺, 靳健, 等. 基于知识图谱的分布式光伏运维方案匹配方法[J]. 计算机集成制造系统, 2021(7): 1860-1870. doi: 10.13196/j.cims.2021.07.002

    OU Y M, SU Y H, J J, et al. A method for matching distributed photovoltaic operation and mainte-nance schemes based on knowledge graph[J]. Computer Integrated Manufacturing Systems, 2021(7): 1860-1870. doi: 10.13196/j.cims.2021.07.002
    [30]
    DING C, LIU J, WANG D Y, et al. A knowledge graph-based muti-agent learning method for dynamic scheduling of flexible job shop[C]//2023 China Automation Congress(CAC). Chongqing, China: IEEE, 2023: 2832-2836.
    [31]
    MITRA D, GUPTA S. Plant disease identification and its solution using machine learning[C]//2022 3rd International Conference on Intelligent Engineering and Management (ICIEM). London, United Kingdom: IEEE, 2022: 152-157.
    [32]
    Liu Z J. Fully Automated CFD Simulation System Research Based on Design Scheme Tree[J/OL]. Scientific Reports: 3975[2025-02-07]. https://doi.org/10.1038/s41598-024-83582-2
    [33]
    STAVRAKOUDIS D G, THEOCHARIS J B. Employing effective feature selection in Genetic Fuzzy Rule-Based Classification Systems[C]//2010 4th International Workshop on Genetic and Evolutionary Fuzzy Systems(GEFS). Mieres, Spain: IEEE, 2010: 21-26.
    [34]
    刘嘉, 黄馨漪, 和志成, 等. 基于统计规则匹配的防火墙优化方案设计[J]. 电子设计工程, 2019, 27(23): 135-138,143. doi: 10.14022/j.issn1674-6236.2019.23.028

    LIU J, HUANG X Y, HE Z C, et al. Design of fire-wall optimization scheme based on statistical rule matching[J]. Electronic Design Engineering, 2019, 27(23): 135-138,143. doi: 10.14022/j.issn1674-6236.2019.23.028
    [35]
    ZHANG X, TAO X H, KONG P, et al. Automatic generation and optimization of power grid operation modes based on production simulation calculation results[C]// 2024 7th International Conference on Energy, Electrical and Power Engineering(CEEPE). Yangzhou, China: IEEE, 2024: 771-777.
    [36]
    何占豪, 杨君刚, 何占豪, 等. 基于事件图谱的作战指挥控制系统构建研究[C]//第十一届中国指挥控制大会论文集, 北京, 中国: 中国指挥与控制学会, 2023: 590-595.
    [37]
    张子伟, 郭齐胜, 董志明, 等. 体系作战效能评估与优化方法综述[J]. 系统仿真学报, 2022(2): 303-313. doi: 10.16182/j.issn1004731x.joss.21-0225

    ZHANG Z W, GUO Q S, DONG Z M, et al. Review on evaluation and optimization methods for system combat effectiveness[J]. Journal of System Simulation, 2022(2): 303-313. doi: 10.16182/j.issn1004731x.joss.21-0225
    [38]
    ZHAO Y, DU K. A Matching Scheme from Supply and Demand Sides of Electronic Health Records Based on Blockchain[C]//2022 7th International Conference on Intelligent Computing and Signal Processing(ICSP). Xi’an, China: IEEE, 2022: 1089-1092.
    [39]
    郭健, 王磊, 张常龙, 等. 基于仿真推演的航天侦察体系效能评估[J]. 信息工程大学学报, 2023, 24(3): 364-369.

    GUO J, WANG L, ZHANG C L, et al. Evaluation of the effectiveness of space reconnaissance system based on simulation analysis[J]. Journal of Information Engineering University, 2023, 24(3): 364-369.
    [40]
    王兴众, 王敏, 罗威, 等. 基于SAC算法的作战仿真推演智能决策技术[J]. 中国舰船研究, 2021, 16(6): 99-108. doi: 10.19693/j.issn.1673-3185.02099

    WANG X Z, WANG M, LUO W, et al. Intelligent decision-making technology for combat simulation and scenario-based analysis based on SAC algorithm[J]. China Ship Research, 2021, 16(6): 99-108. doi: 10.19693/j.issn.1673-3185.02099
    [41]
    熊伟, 于小岚, 刘亚丽, 等. 基于贝叶斯网络的作战效能分析方法[J]. 科学技术与工程, 2023, 23(17): 7428-7435.

    XIONG W, YU X L, LIU Y L, et al. Analysis method of combat effectiveness based on Bayesian network[J]. Science Technology and Engineering, 2023, 23(17): 7428-7435.
    [42]
    Janson Paul, Sivakumar Piraveen, Rajasegaran Jathushan. FewShotNeRF: Meta-learning-based novel view synthesis for rapid scene-specific adaptation[EB/OL]. [2024-08-09]. https://doi.org/10.48550/arXiv.2408.04803.
    [43]
    张涛. 面向装备作战效能评估的多源数据融合方法研究[D]. 长沙: 国防科技大学, 2022
    [44]
    杨文卓. 基于改进鲸鱼算法优化的支持向量机分类器的研究与应用[D]. 武汉: 武汉轻工大学, 2024
    [45]
    汪迪. 基于改进布谷鸟算法优化支持向量机的轴承故障识别研究[D]. 大连: 大连交通大学, 2023
    [46]
    CAI Y H, ZHOU Z Y, LI Z H. Optimization study of BP neural network based on genetic algorithm[C]// 2023 IEEE International Conference on Electrical, Automation and Computer Engineering (ICEACE). Changchun, China: IEEE, 2023: 1555-1560.
    [47]
    曹同宇, 乔栋, 郭子瑜, 等. 基于改进蜣螂优化算法优化BP神经网络[J]. 无线互联科技, 2024, 21(22): 109-114.

    CAO T Y, QIAO D, GUO Z Y, et al. Optimization of BP neural network based on improved antlion optimization algorithm[J]. Wireless Internet Technology, 2024, 21(22): 109-114.
    [48]
    PAULOSKI J G, ZHANG Z, HUANG L, et al. Convolutional neural network training with distributed K-FAC[EB/OL]. [2020-07-01]. https://arxiv.org/abs/2007.00784
    [49]
    BARRIGA N A, STANESCU M, BESOAIN F. Improving RTS game AI by supervised policy learning, tactical search, and deep reinforcement learning[J]. Institute of Electrical and Electronics Engineers(IEEE), 2019, 14(3): 8-18.
    [50]
    张成苗. 基于强化学习的智能抗干扰决策方案[D]. 西安: 西安电子科技大学, 2022
    [51]
    潘长鹏, 王中发, 王海涛, 等. 基于BP神经网络的舰载机对陆打击作战效能评估[J]. 兵工自动化, 2022, 41(12): 9-12.

    PAN C P, WANG Z F, WANG H T, et al. Evaluation of the combat effectiveness of carrier-based air-craft for ground attack based on BP neural network[J]. Automation of Military Industry, 2022, 41(12): 9-12.
    [52]
    BARRIGA N A, STANESCU M, BESOAIN F. Improving RTS game AI by supervised policy learning, tactical search, and deep reinforcement learning[J]. IEEE Computational Intelligence Magazine, 2019, 14(3): 8-18. doi: 10.1109/MCI.2019.2919363
    [53]
    丁沛灏. 基于深度学习的长时间序列预测方法研究与应用[D]. 重庆: 西南大学, 2024
    [54]
    孙志鹏. 基于网约车行程时间预测的多方式协同出行方案推荐及优化[D]. 西安: 长安大学, 2021
    [55]
    张尧. 激活函数导向的RNN算法优化[D]. 杭州: 浙江大学, 2017
    [56]
    张尧, 沈海斌. 非饱和区扩展的RNN算法优化[J]. 传感器与微系统, 2018, 37(3): 41-43. doi: 10.13873/j.1000-9787(2018)03-0041-03

    ZHANG Y, SHEN H B. Optimization of RNN algorithm for non-saturated zone expansion[J]. Sensors and Microsystems, 2018, 37(3): 41-43. doi: 10.13873/j.1000-9787(2018)03-0041-03
    [57]
    袁琳娜, 杨良斌. 基于APSO的LSTM神经网络模型优化方法研究[J]. 重庆大学学报, 2024, 47(8): 103-111.

    Yuan Linna, Yang Liangbin. Research on optimization method of LSTM neural network model based on APSO[J]. Journal of Chongqing University, 2024, 47(8): 103-111.
    [58]
    孙怡峰, 李智, 吴疆, 等. 作战方案驱动的可学习兵棋推演智能体研究[J]. 系统仿真学报, 2024, 36(7): 1525-1535. doi: 10.16182/j.issn1004731x.joss.23-0477

    SUN Y F, LI Z, WU J, et al. Research on learning-based intelligent agents for war gaming driven by combat plans[J]. Journal of System Simulation, 2024, 36(7): 1525-1535. doi: 10.16182/j.issn1004731x.joss.23-0477
    [59]
    COUSSEMENT K, ABEDIN M Z, KRAUS M, et al. Explainable AI for enhanced decision-making[J]. Decision Support Systems, 2024, 184: 114276. doi: 10.1016/j.dss.2024.114276
    [60]
    Jin W Q, Du H Y, Zhao B. A comprehensive survey on multi-agent cooperative decision-making: Scenarios, approaches, challenges and perspectives[EB/OL]. [2025-03-17]. https://arxiv.org/abs/2503.13415.
    [61]
    Yu C Y, Mao Z Y, Wu Y L, et al. BA-GPT: Battlefield awareness interactive Q&A system based on RAG[C]// Proceedings of 4th 2024 International Conference on Autonomous Unmanned Systems(4th ICAUS 2024). Hangzhou, China: Chinese Academy of Engineering, 2024.
    [62]
    PAMUNGKAS R F, UTAMA I B K Y, HINDRIYANDHITO K, et al. A hybrid approach of Con-vLSTMBNN-DT and GPT-4 for real-time anomaly detection decision support in edge–cloud[J]. ICT Express, 2024, 10(5): 1026-1033. doi: 10.1016/j.icte.2024.07.007
    [63]
    WU H Y, LI S Y, WU D R. TMMM: Transformer in multimodal sentiment analysis under missing modalities[C]// 2024 International Joint Conference on Neural Networks (IJCNN). Yokohama, Japan: IEEE, 2024: 1-8.
    [64]
    HU M. Planning with a model: AlphaZero[M]. Berkeley, CA, USA: Springer Nature, 2023: 245-280.
    [65]
    GOECKS V G, WAYTOWICH N. COA-GPT: Generative pre-trained transformers for accelerated course of action development in military operations[C]//2024 International Conference on Military Communication and Information Systems (ICMCIS). Koblenz, Germany: IEEE, 2024.
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