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Computational Intelligence Based Complex Adaptive System-of-System Architecture Evolution Strategy

  • Siddhartha AgarwalEmail author
  • Cihan H. Dagli
  • Louis E. PapeII
Conference paper

Abstract

There is a constant challenge to incorporate new systems and upgrade existing systems under threats, constrained budget and uncertainty into systems of systems (SoS). It is necessary for program managers to be able to assess the impacts of future technology and stakeholder changes. This research helps analyze sequential decisions in an evolving SoS architecture through three key features: SoS architecture generation, assessment and implementation through negotiation. Architectures are generated using evolutionary algorithms and assessed using type II fuzzy nets. The approach accommodates diverse stakeholder views, converting them to key performance parameters (KPPs) for architecture assessment. It is not possible to implement an acknowledged SoS architecture without persuading the systems to participate. A negotiation model is proposed to help the SoS manger adapt his strategy based on system owners’ behavior. Viewpoints of multiple stakeholders are aggregated to assess the overall mission effectiveness of an architecture against the overarching objectives. A search and rescue (SAR) example illustrates application of the method. Future research might include group decision making for evaluating architectures.

Keywords

Architecture Acquisition Evolutionary algorithms Machine learning Systems of systems Meta-Architectures 

Notes

Acknowledgment

This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Systems Engineering Research Center (SERC) under Contract H98230-08-D-0171. SERC is a federally funded University Affiliated Research Center managed by Stevens Institute of Technology. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Department of Defense.

References

  1. 1.
    Trentesaux, D., Knothe, T., Branger, G., Fischer, K.: Planning and control of maintenance, repair and overhaul operations of a fleet of complex transportation systems: a cyber-physical system approach. In: Service Orientation in Holonic and Multi-agent Manufacturing, pp. 175–186. Springer International Publishing (2015)Google Scholar
  2. 2.
    Obal, L., Lin, F.: A framework for healthcare information systems: exploring a large system of systems using system dynamics. Commun. IIMA 5(3), 4 (2015)Google Scholar
  3. 3.
    Maia, P., Cavalcante, E., Gomes, P., Batista, T., Delicato, F. C., Pires, P. F.: On the development of systems-of-systems based on the internet of things: a systematic mapping. In: Proceedings of the 2014 European Conference on Software Architecture Workshops, p. 23. ACM, August 2014Google Scholar
  4. 4.
    Martí, J., Ventura, C., Hollman, J., Srivastava, K., Juarez, H.: I2Sim modelling and simulation framework for scenario development, training, and real-time decision support of multiple interdependent critical infrastructures during large emergencies. In: NATO (OTAN) MSG-060 Symposium on “How is Modelling and Simulation Meeting the Defence Challenges out to 2015?” (2015)Google Scholar
  5. 5.
    Nahavandi, S., Creighton, D., Le, V.T., Johnstone, M., Zhang, J.: Future integrated factories: a system of systems engineering perspective. In: Integrated Systems: Innovations and Applications, pp. 147–161. Springer International Publishing (2015)Google Scholar
  6. 6.
    Zhang, L.: Applying system of systems engineering approach to build complex cyber physical systems. In: Progress in Systems Engineering, pp. 621–628. Springer International Publishing (2015)Google Scholar
  7. 7.
    Dong, P., Han, Y., Guo, X., Xie, F.: A systematic review of studies on cyber physical system security. Int. J. Secur. Appl. 9(1), 155–164 (2015)Google Scholar
  8. 8.
    Malan, R., Bredemeyer, D.: Architecture resources, defining non-functional requirements (2001)Google Scholar
  9. 9.
    Jamishidi, M.: System of systems-innovations for 21st century. In: IEEE Region 10 and the Third international Conference on Industrial and Information Systems, 2008, ICIIS 2008, pp. 6–7. IEEE, December 2008Google Scholar
  10. 10.
    Arnold, A., Boyer, B., Legay, A.: Contracts and behavioral patterns for SoS: the EU IP DANSE approach. arXiv preprint arXiv:1311.3631 (2013)
  11. 11.
    Paulen, R., Engell, S.: DYMASOS—dynamic management of physically coupled systems of systems, published on ERCIM News 97, April 2014, Special theme: Cyber-Physical Systems, February 25, 2014Google Scholar
  12. 12.
    Coleman, J.W., Malmos, A.K., Larsen, P.G., Peleska, J., Hains, R.: COMPASS tool vision for a system of systems collaborative development environment. In: International Conference on System of Systems Engineering, 2012 7th International Conference on, pp. 451–456, 16–19 (2012)Google Scholar
  13. 13.
    Agarwal, S., Ganguli, R.: Refining automated modeling of operational data by identifying the most important input factors. Min. Eng. 63(12), 52–54 (2011)Google Scholar
  14. 14.
    Pape, L., Agarwal, S., Giammarco, K., Dagli, C.: Fuzzy optimization of acknowledged system of systems meta-architectures for agent based modeling of development. Procedia Comput. Sci. 28, 404–411 (2014)CrossRefGoogle Scholar
  15. 15.
    Konur, D., Dagli, C. (2014). Military system of systems architecting with individual system contracts. In: Optimization Letters, pp. 1–19Google Scholar
  16. 16.
    Agarwal, S., Pape, L.E., Dagli, C.H.: A hybrid genetic algorithm and particle swarm optimization with type-2 fuzzy sets for generating systems of systems architectures. Procedia Comput. Sci. 36(133), 57–64 (2014)CrossRefGoogle Scholar
  17. 17.
    Pape, L., Giammarco, K., Colombi, J., Dagli, C., Kilicay-Ergin, N., Rebovich, G.: A fuzzy evaluation method for system of systems meta-architectures. Procedia Comput. Sci. 16, 245–254 (2013)CrossRefGoogle Scholar
  18. 18.
    Ergin, N.K.: Improving collaboration in search and rescue system of systems. Procedia Comput. Sci. 36, 13–20 (2014)CrossRefGoogle Scholar
  19. 19.
    Wang, R., Dagli, C.H.: Executable system architecting using systems modeling language in conjunction with colored Petri nets in a model-driven systems development process. Syst. Eng. 14(4), 383–409 (2011)CrossRefGoogle Scholar
  20. 20.
    Wang, R., Agarwal, S., Dagli, C.: Executable system of systems architecture using OPM in conjunction with colored Petri Net: a module for flexible intelligent and learning architectures for system of systems. In: Europe Middle East & Africa Systems Engineering Conference (EMEASEC) (2014)Google Scholar
  21. 21.
    Agarwal, S., Wang, R., Dagli, C.: FILA-SoS, executable architectures using cuckoo search optimization coupled with OPM and CPN-A module: a new meta-architecture model for FILA-SoS, France. In: Boulanger, F., Krob, D., Morel, G., Roussel (eds.) Jean-Claude Complex Systems Design and Management (CSD&M), pp. 175–192. Springer International Publishing (2015)Google Scholar
  22. 22.
    Agarwal, S., Pape, L.E., Dagli, C.H., Ergin, N.K., Enke, D., Gosavi, A., Qin, R., Konur, D., Wang, R., Gottapu, R.D.: Flexible intelligent learning architectures for SoS (FILA-SoS): architectural evolution in systems of systems. Procedia Comput. Sci. 44(2015), 76–85 (2015)CrossRefGoogle Scholar
  23. 23.
    Schutze, O., Lara, A., Coello, C.A.: On the influence of the number of objectives on the hardness of a multi objective optimization problem. Evol. Comput. IEEE Trans. 15(4), 444–455 (2011)CrossRefGoogle Scholar
  24. 24.
    Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Evolutionary many-objective optimization: a short review. In: IEEE Congress on Evolutionary Computation, pp. 2419–2426, June 2008Google Scholar
  25. 25.
    Coello, C.A.C.: An updated survey of evolutionary multi objective optimization techniques: state of the art and future trends. In: CEC 99. Proceedings of the 1999 Congress on Evolutionary Computation, 1999, vol. 1. IEEE (1999)Google Scholar
  26. 26.
    Agarwal, S.: Computational intelligence based complex adaptive system-of-system architecture evolution strategy. In: Ph.D. Dissertation, Missouri University of Science and Technology, May 2015Google Scholar
  27. 27.
    Singh, A., Dagli, C.H.: “Computing with words” to support multi-criteria decision-making during conceptual design. In: Systems Research Forum, pp. 85–99 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Siddhartha Agarwal
    • 1
    Email author
  • Cihan H. Dagli
    • 1
  • Louis E. PapeII
    • 1
  1. 1.Missouri University of Science and TechnologyRollaUSA

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