Designing Cyber-physical Systems with Evolutionary Algorithms
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).
KeywordsCyper-physical systems Model-based design CPS integration Optimization Evolutionary algorithms Emergent behavior
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.
- 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
- 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
- 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
- Elmenreich, W., Ibounig, T., & Fehervari, I. (2009). Robustness versus performance in sorting and tournament algorithms. Acta Polytecnica, 6(5), 7–18.Google Scholar
- Fehérvári, I. (2013). On evolving self-organizing technical systems. Ph.D. thesis, Institute of Networked and Embedded Systems, Alpen-Adria-Universität Klagenfurt.Google Scholar
- Fehérvári, I., & Elmenreich, W. (2009). Towards evolving cooperative behavior with neural controllers. In IFIP Fourth International Workshop on Self-Organizing Systems.Google Scholar
- Fehérvári, I., & Elmenreich, W. (2010b). FREVO – Framework for evolutionary design. http://frevo.sourceforge.net/. Accessed 06 May 2017.
- Foundation, N. S. (2016). Cyber physical systems. https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=503286. Accessed 17 Apr 2017.
- 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
- 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
- Rechenberg, I. (1994). Evolutionsstrategie’94. Stuttgart: Frommann-Holzboog.Google Scholar
- Resnick, M. (1997). Turtles, termites, and traffic jams: Explorations in massively parallel microworlds (Complex adaptive systems). Cambridge: MIT Press.Google Scholar
- 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
- 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
- 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
- Yang, X.-S. (2008). Nature-inspired metaheuristic algorithms. Frome: Luniver Press.Google Scholar