{"title":"Architecting Path Selection Method for Incremental Evolution in System-of-Systems","authors":"Zhemei Fang;Dazhi Chen;Qi Ju;Jianbo Wang","doi":"10.1109/JSYST.2025.3553965","DOIUrl":null,"url":null,"abstract":"Architecture design for system-of-systems (SoSs) is a complex challenge due to interdependencies, uncertainties, and the large design space. The evolutionary nature of SoSs necessitates a multistage architecting process, adding further complexity. This article, thus, proposes a deep reinforcement learning based evolutionary architecture path selection method that considers uncertainties and interdependency. The approach employs an architecture framework to guide the design and defines SoS architecture decisions as the addition of systems and the allocation of operational architecture to physical architecture across sequential stages. Capability evaluation leverages a capability-activity-system structure, supported by a functional dependency network analysis method. Utilizing a deep neural network as a functional approximator to predict future SoS capability, the article develops a proximal policy optimization (PPO) algorithm that balances immediate and future needs. Applied to a mosaic warfare-oriented naval antisubmarine SoS, the proposed method outperforms heuristic optimization techniques by achieving higher SoS capability, reduced instability, and fewer violations of budget and intermediate requirements constraints in both deterministic and stochastic scenarios. These results highlight the PPO method's effectiveness in addressing SoS architecting path selection challenges under uncertainty.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 2","pages":"636-647"},"PeriodicalIF":4.4000,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Systems Journal","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10959071/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Architecture design for system-of-systems (SoSs) is a complex challenge due to interdependencies, uncertainties, and the large design space. The evolutionary nature of SoSs necessitates a multistage architecting process, adding further complexity. This article, thus, proposes a deep reinforcement learning based evolutionary architecture path selection method that considers uncertainties and interdependency. The approach employs an architecture framework to guide the design and defines SoS architecture decisions as the addition of systems and the allocation of operational architecture to physical architecture across sequential stages. Capability evaluation leverages a capability-activity-system structure, supported by a functional dependency network analysis method. Utilizing a deep neural network as a functional approximator to predict future SoS capability, the article develops a proximal policy optimization (PPO) algorithm that balances immediate and future needs. Applied to a mosaic warfare-oriented naval antisubmarine SoS, the proposed method outperforms heuristic optimization techniques by achieving higher SoS capability, reduced instability, and fewer violations of budget and intermediate requirements constraints in both deterministic and stochastic scenarios. These results highlight the PPO method's effectiveness in addressing SoS architecting path selection challenges under uncertainty.
期刊介绍:
This publication provides a systems-level, focused forum for application-oriented manuscripts that address complex systems and system-of-systems of national and global significance. It intends to encourage and facilitate cooperation and interaction among IEEE Societies with systems-level and systems engineering interest, and to attract non-IEEE contributors and readers from around the globe. Our IEEE Systems Council job is to address issues in new ways that are not solvable in the domains of the existing IEEE or other societies or global organizations. These problems do not fit within traditional hierarchical boundaries. For example, disaster response such as that triggered by Hurricane Katrina, tsunamis, or current volcanic eruptions is not solvable by pure engineering solutions. We need to think about changing and enlarging the paradigm to include systems issues.