## 5 Conclusions  
Delivering value persistently is a critical requirement for a combat SoS under fierce confrontation. The characteristics of SoSs provide a good opportunity to reconfigure the architecture during failures in operation with inherent features such as functional redundancy and substitution. Such an operation requires an appropriate architecture design at the beginning. This paper develops a quantitative architecture reconfiguration framework to enhance SoS resilience by considering the initial architecture development and architecture reconfiguration in an integrated manner. This framework considers DoDAF-based SoS architecture configuration. The resulting SoS effectiveness is evaluated using an extended three-layer functional dependency network analysis method. The effectiveness evaluator supports the formulation and solution development for optimal SoS architecture reconfiguration. The proposed framework is then applied to a synthetic naval air and missile defense SoS. The results provide decision-makers with a Pareto optimal set of solutions that have non-dominated recoverability and survivability. The decision-makers can then select the SoS composition and initial architecture configuration based on the suggestion and their own preferences and respond quickly to disruptions by computing the reconfiguration decisions. Future work will focus on two perspectives. One perspective concerns theoretical enrichment from two directions. The first direction focuses on the side of architecture design. We plan to study the architecture reconfiguration enablers in depth, and develop quantitative evaluation metrics for the elements of reconfigurability. The reconfigurability metrics can be validated by the reconfiguration process during the operation phase and validated metrics can guide the architecture design as well. The second direction will concentrate on the reconfiguration process. The focus is on the solution algorithm aspect. We will conduct more thorough experiments to figure out the most suitable intelligent algorithm (e.g., particle swarm optimization, simulated annealing) and explore the approximation techniques to replace the genetic algorithm for the inner-loop reconfiguration process to increase the computational efficiency. Approximate dynamic programming or reinforcement learning might be a promising framework to manage the two-stage problem. However, it is noteworthy that the focus of the proposed method is more on the design phase than the operational phase, which reduces the impact of computational efficiency. The other perspective concerns extending the case study to include larger, real-world datasets to validate the proposed framework in operational scenarios. Additional sensitivity analyses of parameters (e.g., acquisition cost, communication bandwidth) are also necessary to highlight robustness under different scenarios. Conceptualization, Z.F., H.L. and D.C.; methodology, Z.F., H.L. and D.C.; validation, D.C. and Z.F. ; writing--original draft preparation, Z.F. ; writing--review and editing, H.L. ; visualization, D.C.; supervision, Z.F. ; project administration, Z.F. ; funding acquisition, Z.F. and H.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the National Natural Science Foundation of China under Grant No. 62103158. **Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. **DURC Statement:** Current research is limited to an integrated approach for selecting architecture design alternatives with potential reconfigurable options to achieve better resilience. As an important topic in the field of system architecture design and system-of-systems engineering, the method is beneficial for designing complex systems that are expected to deliver long-term values and does not pose a threat to public health or national security.