Systems Engineering and Electronics ›› 2026, Vol. 48 ›› Issue (6): 1980-1990.doi: 10.12305/j.issn.1001-506X.2026.06.19

• Systems Engineering • Previous Articles     Next Articles

Online reconstruction method for strike chain in dynamic confrontation scenarios

Yuyang XUE1,2,3(), Tao WANG1,2,3,*, Xin LIAO4, Yuannan XU4, Minghao LIN1,2,3, Anqi WU1,2,3   

  1. 1. School of Intelligent Systems Engineering,Sun Yat-sen University,Shenzhen 517108,China
    2. Southern Marine Science and Engineering Guangdong Laboratory,Zhuhai 519000,China
    3. Center for Swarm Intelligence,Institute of Artificial Intelligence,Sun Yat-sen University,Guangzhou 510275,China
    4. Research and Development Center,China Academy of Launch Vehicle Technology,Beijing 100076,China
  • Received:2025-08-18 Revised:2025-11-04 Accepted:2025-12-16 Online:2026-06-25 Published:2026-03-20
  • Contact: Tao WANG E-mail:xueyy7@mail2.sysu.edu.cn

Abstract:

To address the problem of strike chain interruption caused by node damage in dynamic confrontation scenarios, an online reconstruction method based on breakpoint reuse and local search is proposed. A three-layer “physical–functional–state” abstract network model is constructed to achieve unified modeling of nodes, links, and semantic constraints. An improved non-dominated sorting genetic algorithm (NSGA)-II is introduced to retain usable front-end links and compress the search space, and the technique for order preference by similarity to ideal solution (TOPSIS) is combined to enable millisecond-level Pareto-optimal decision-making. Simulation results show that the proposed method reduces link reconstruction latency by 62% compared with traditional strategies, and maintains a very high mission completion rate even when equipment resources are reduced by 20%. Moreover, the algorithm complexity increases linearly with node scale, meeting the real-time and scalability requirements of mainstream distributed combat systems.

Key words: dynamic confrontation, strike chain, online reconstruction, multi-objective optimization, non-dominated sorting genetic algorithm (NSGA)-II, technique for order preference by similarity to ideal solution (TOPSIS)

CLC Number: 

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