基于强化学习的装备体系韧性优化方法

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中图分类号： E917 文献标志码：A DOI：10.12305/j.issn. 1001-506X.2025.07.15

Abstract： The equipment system-of-systems （ESoS） inevitably is affected by disturbance events such as external atacks and internal failures in actual operation，causing multiple equipment node failures. How to scientifically and rationally formulate recovery strategies to quickly restore system capabilities and enhance the resilience of the ESoS has important military value and significance. Based on this，this paper proposes an ESoS resilience optimization method based on reinforcement learning. Firstly，the ESoS resilience measurement index is established by integrating network topology and network performance parameters.Secondly，a reinforcement learning algorithm based on Q -Learning node recovery sequence is proposed，and different disturbance scenarios are used to test the change of resilience. Finally，combined with typical cases to verify the feasibility and effectiveness of the proposed algorithm. Through comparative experiments with empirical recovery strategies and genetic algorithm，the results show that with deliberate attcks，the toughness value obtained based on reinforcement learning is 37.46% higher than that based on node ability importance priority recovery strategy， degree priority recovery strategy and random recovery strategy 52.28% and 85.65% ; compared with the genetic algorithm，the resilience value obtained after optimization increased by 28.72% . The above analysis effectively shows the superiority of the proposed method and model.

Keywords：equipment system-of-systems （ESoS）； reinforcement learning； resilience optimization;recovery strategy

0 引言

智能化背景下的装备体系从固定组合、预先筹划向动态调整规划、快速适应发展，不再单纯追求硬杀伤，而是对作战网络进行调整以保障持续完成使命任务[1]。（剩余14985字）