IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i10p1476-d1391431.html
   My bibliography  Save this article

Optimal Unmanned Combat System-of-Systems Reconstruction Strategy with Heterogeneous Cost via Deep Reinforcement Learning

Author

Listed:
  • Ruozhe Li

    (National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China)

  • Hao Yuan

    (National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China)

  • Bangbang Ren

    (National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China)

  • Xiaoxue Zhang

    (National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China)

  • Tao Chen

    (National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China)

  • Xueshan Luo

    (National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China)

Abstract

The unmanned combat system-of-systems (UCSoS) in modern warfare is comprised of various interconnected entities that work together to support mission accomplishment. The soaring number of entities makes the UCSoS fragile and susceptible to triggering cascading effects when exposed to uncertain disturbances such as attacks or failures. Reconfiguring the UCSoS to restore its effectiveness in a self-coordinated and adaptive manner based on the battlefield situation and operational requirements has attracted increasing attention. In this paper, we focus on the UCSoS reconstruction with heterogeneous costs, where the collaboration nodes may have different reconstruction costs. Specifically, we adopt the heterogeneous network to capture the interdependencies among combat entities and propose a more representative metric to evaluate the UCSoS reconstruction effectiveness. Next, we model the combat network reconstruction problem with heterogeneous costs as a nonlinear optimization problem and prove its NP-hardness. Then, we propose an approach called SoS-Restorer, which is based on deep reinforcement learning (DRL), to address the UCSoS reconstruction problem. The results show that SoS-Restorer can quickly generate reconstruction strategies and improve the operational capabilities of the UCSoS by about 20∼60% compared to the baseline algorithm. Furthermore, even when the size of the UCSoS exceeds that of the training data, SoS-Restorer exhibits robust generalization capability and can efficiently produce satisfactory results in real time.

Suggested Citation

  • Ruozhe Li & Hao Yuan & Bangbang Ren & Xiaoxue Zhang & Tao Chen & Xueshan Luo, 2024. "Optimal Unmanned Combat System-of-Systems Reconstruction Strategy with Heterogeneous Cost via Deep Reinforcement Learning," Mathematics, MDPI, vol. 12(10), pages 1-23, May.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:10:p:1476-:d:1391431
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/10/1476/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/10/1476/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fan, Dongming & Sun, Bo & Dui, Hongyan & Zhong, Jilong & Wang, Ziyao & Ren, Yi & Wang, Zili, 2022. "A modified connectivity link addition strategy to improve the resilience of multiplex networks against attacks," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Le & Du, Ye, 2023. "Cascading failure model and resilience enhancement scheme of space information networks," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    2. Zhang, Hui & Xu, Min & Ouyang, Min, 2024. "A multi-perspective functionality loss assessment of coupled railway and airline systems under extreme events," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. Watson, Bryan C & Morris, Zack B & Weissburg, Marc & Bras, Bert, 2023. "System of system design-for-resilience heuristics derived from forestry case study variants," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    4. Li, Jiahui & Qi, Xiaogang & He, Yi & Liu, Lifang, 2024. "SDN candidate and protection path selection for link failure protection in hybrid SDNs," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    5. Li, Hongxu & Sun, Qin & Zhong, Yuanfu & Huang, Zhiwen & Zhang, Yingchao, 2023. "A soft resource optimization method for improving the resilience of UAV swarms under continuous attack," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    6. Li, Sheng & Liu, Wenwen & Wu, Ruizi & Li, Junli, 2023. "An adaptive attack model to network controllability," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    7. Xu, Mengqiao & Deng, Wenhui & Zhu, Yifan & LÜ, Linyuan, 2023. "Assessing and improving the structural robustness of global liner shipping system: A motif-based network science approach," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    8. Liu, Meili & Qi, Xiaogang & Pan, Hao, 2022. "Optimizing communication network geodiversity for disaster resilience through shielding approach," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    9. Cui, Hongjun & Wang, Fei & Ma, Xinwei & Zhu, Minqing, 2022. "A novel fixed-node unconnected subgraph method for calculating the reliability of binary-state networks," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    10. Ding, Xiao & Wang, Huan & Zhang, Xi & Ma, Chuang & Zhang, Hai-Feng, 2024. "Dual nature of cyber–physical power systems and the mitigation strategies," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    11. Liu, Yang & Ma, Xiaoxue & Qiao, Weiliang & Ma, Laihao & Han, Bing, 2024. "A novel methodology to model disruption propagation for resilient maritime transportation systems–a case study of the Arctic maritime transportation system," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    12. Dong, Shangjia & Gao, Xinyu & Mostafavi, Ali & Gao, Jianxi & Gangwal, Utkarsh, 2023. "Characterizing resilience of flood-disrupted dynamic transportation network through the lens of link reliability and stability," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    13. Yue, Xiongping & Mu, Dong & Wang, Chao & Ren, Huanyu & Peng, Rui & Du, Jianbang, 2024. "Critical risks in global supply networks: A static structure and dynamic propagation perspective," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    14. Chen, Zhiwei & Hong, Dongpao & Cui, Weiwei & Xue, Weikang & Wang, Yao & Zhong, Jilong, 2023. "Resilience evaluation and optimal design for weapon system of systems with dynamic reconfiguration," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    15. Sun, Hao & Yang, Ming & Wang, Haiqing, 2022. "A virtual experiment for measuring system resilience: A case of chemical process systems," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    16. Wang, Nanxi & Yuen, Kum Fai, 2022. "Resilience assessment of waterway transportation systems: Combining system performance and recovery cost," Reliability Engineering and System Safety, Elsevier, vol. 226(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:10:p:1476-:d:1391431. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.