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Designing Cyber-physical Systems with Evolutionary Algorithms

  • Melanie SchranzEmail author
  • Wilfried Elmenreich
  • Micha Rappaport
Chapter

Abstract

Cyber physical systems (CPSs) find their application in different domains, including smart cities, Internet of Things (IoT), and Industry 4.0. The increasing degree of interaction among CPSs leads to unpredictable and partially unexpected behavior. The major steps to manage emerging behavior in CPSs are taken in the design process. Although a high number of methods and tools already exist from related disciplines (including complex system research, embedded system design, and self-organization), there is no comprehensive toolset available to address the extensive CPS design process. This chapter presents a proposal for a common CPS design toolset. It combines existing and emerging tools to design, simulate, evaluate, and deploy solutions for complex, real-world problems using evolutionary algorithms on the example of swarms of unmanned aerial vehicles (UAVs).

Keywords

Cyper-physical systems Model-based design CPS integration Optimization Evolutionary algorithms Emergent behavior 

Notes

Acknowledgements

We are grateful to Andreas Kercek and Christian Raffelsberger for their critical comments on this chapter. Further we would like to thank Angelika Schauer for proofreading the text. The research leading to these results has received funding from the European Union Horizon 2020 research and innovation program under grant agreement No 731946.

References

  1. Bagnato, A., Biró, R. K., Bonino, D., Pastrone, C., Elmenreich, W., Reiners, R., Schranz, M., & Arnautovic, E. (2017). Designing swarms of cyber-physical systems: the H2020 CPSwarm project. In Proceedings of the ACM International Conference on Computing Frontiers.Google Scholar
  2. Cohen, I., Corman, D., Davis, J., Khurana, H., Mosterman, P. J., Prasad, V., & Stormo, L. (2013). Strategic R&D opportunities for 21st century cyber-physical systems. Technical report, National Institute of Standards and Technology – Steering Committee for Foundations in Innovation for Cyber-Physical Systems.Google Scholar
  3. Elmenreich, W., & Fehérvári, I. (2011). Evolving self-organizing cellular automata based on neural network genotypes. In Proceedings of the Fifth International Workshop on Self-Organizing Systems (LNCS, Vol. 6557, pp. 16–25). Springer.CrossRefGoogle Scholar
  4. Elmenreich, W., & Klingler, G. (2007). Genetic evolution of a neural network for the autonomous control of a four-wheeled robot. In Proceedings of the 6th Mexican International Conference on Artificial Intelligence – Special Session (MICAI) (pp. 396–406).Google Scholar
  5. Elmenreich, W., Ibounig, T., & Fehervari, I. (2009). Robustness versus performance in sorting and tournament algorithms. Acta Polytecnica, 6(5), 7–18.Google Scholar
  6. Fehérvári, I. (2013). On evolving self-organizing technical systems. Ph.D. thesis, Institute of Networked and Embedded Systems, Alpen-Adria-Universität Klagenfurt.Google Scholar
  7. Fehérvári, I., & Elmenreich, W. (2009). Towards evolving cooperative behavior with neural controllers. In IFIP Fourth International Workshop on Self-Organizing Systems.Google Scholar
  8. Fehérvári, I., & Elmenreich, W. (2010a). Evolving neural network controllers for a team of self-organizing robots. Journal of Robotics, 2010(1), 1–10.CrossRefGoogle Scholar
  9. Fehérvári, I., & Elmenreich, W. (2010b). FREVO – Framework for evolutionary design. http://frevo.sourceforge.net/. Accessed 06 May 2017.
  10. Fehérvári, I., & Elmenreich, W. (2014). Evolution as a tool to design self-organizing systems. In Self-organizing systems (Vol. 8221, pp. 139–144). Berlin/Heidelberg: Springer.CrossRefGoogle Scholar
  11. Floreano, D., & Urzelai, J. (2000). Evolutionary robots with on-line self-organization and behavioral fitness. Neural Networks, 13(4–5), 431–443.CrossRefGoogle Scholar
  12. Foundation, N. S. (2016). Cyber physical systems. https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=503286. Accessed 17 Apr 2017.
  13. Greenfield, G. (2012). A platform for evolving controllers for simulated drawing robots. In Evolutionary and biologically inspired music, sound, art and design (Lecture notes in computer science, Vol. 7247, pp. 108–116). Berlin/Heidelberg: Springer.CrossRefGoogle Scholar
  14. Lee, E. A. (2008). Cyber physical systems: design challenges. In Proceedings of the 11th IEEE Symposium on Object Oriented Real-Time Distributed Computing (pp. 363–369).Google Scholar
  15. Martins, P. M., & McCann, J. A. (2017). Network-wide programming challenges in cyber-physical systems. In Cyber-physical systems (pp. 103–113).CrossRefGoogle Scholar
  16. Nelson, A. L., Barlow, G. J., & Doitsidis, L. (2009). Fitness functions in evolutionary robotics: A survey and analysis. Robotics and Autonomous Systems, 57(4), 345–370.CrossRefGoogle Scholar
  17. Pintér-Bartha, A., Sobe, A., & Elmenreich, W. (2012). Towards the light – comparing evolved neural network controllers and finite state machine controllers. In Proceedings of the 10th International Workshop on Intelligent Solutions in Embedded Systems (pp. 83–87). Klagenfurt.Google Scholar
  18. Rechenberg, I. (1994). Evolutionsstrategie’94. Stuttgart: Frommann-Holzboog.Google Scholar
  19. Resnick, M. (1997). Turtles, termites, and traffic jams: Explorations in massively parallel microworlds (Complex adaptive systems). Cambridge: MIT Press.Google Scholar
  20. Schätz, B., Törngren, M., Bensalem, S., Cengarle, M. V., Pfeifer, H., McDermid, J., Passerone, R., & Sangiovanni-Vincentelli, A. L. (2015). Research agenda and recommendations for action. Technical report, CyPhERS – Cyber-Physical European Roadmap & Strategy.Google Scholar
  21. Sobe, A., Fehérvári, I., & Elmenreich, W. (2012). Frevo: A tool for evolving and evaluating self-organizing systems. In Proceedings of the 6th IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops (pp. 105–110).Google Scholar
  22. Stanley, K. O., & Miikkulainen, R. (2002). Evolving neural networks through augmenting topologies. Evolutionary Computation, 10(2), 99–127.CrossRefGoogle Scholar
  23. Törngren, M., Bensalem, S., McDermid, J., Passerone, R., Pfeifer, H., Sangiovanni-Vincentelli, A., ... & Asplund, F. (2017). Characterization, analysis and recommendations for exploiting the opportunities of cyber-physical systems. Chapter in the book on Cyber-physical systems: foundations, principles and applications.CrossRefGoogle Scholar
  24. Wahab, M. N. A., Nefti-Meziani, S., & Atyabi, A. (2015). A comprehensive review of swarm optimization algorithms. PLoS One, 10(5), e0122827.CrossRefGoogle Scholar
  25. Yang, X.-S. (2008). Nature-inspired metaheuristic algorithms. Frome: Luniver Press.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Melanie Schranz
    • 1
    Email author
  • Wilfried Elmenreich
    • 2
  • Micha Rappaport
    • 1
  1. 1.Lakeside LabsKlagenfurtAustria
  2. 2.Alpen-Adria-Universität KlagenfurtKlagenfurtAustria

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