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Differential Evolution in Production Process Optimization of Cyber Physical Systems

  • Katharina Giese
  • Jens Eickmeyer
  • Oliver Niggemann
Conference paper
Part of the Technologien für die intelligente Automation book series (TIA, volume 11)

Abstract

In this paper, the application of Differential Evolution in machine optimization is introduced. This enables the optimization of different production processes in modern industrial machines, without having in depth knowledge of the inner workings of production units. Therefor, sensor data is recorded and certain properties like manufacturing time or quality are introduced as new fitness criteria for the evolutionary computing algorithm. This is demonstrated in an exemplary use case for injection moulding. Furthermore, a concept for constant production process stabilization is presented for future research.

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References

  1. 1. R. Storn and K. Price, “Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces,” Journal of Global Optimization, pp. 1–15, 1995.Google Scholar
  2. 2. H. Bersini, M. Dorigo, S. Langerman, G. Seront, and L. Gambardella, “Results of the first international contest on evolutionary optimisation (1st iceo),” in Evolutionary Computation, 1996., Proceedings of IEEE International Conference on. IEEE, 1996, pp. 611–615.Google Scholar
  3. 3. J. Vesterstrom and R. Thomsen, “A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems,” in Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753), vol. 2, June 2004, pp. 1980–1987 Vol.2.Google Scholar
  4. 4. S. Das and P. N. Suganthan, “Differential evolution: A survey of the state-of-theart,” IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp. 4–31, Feb 2011.CrossRefGoogle Scholar
  5. 5. Y. Liu and F. Sun, “A fast differential evolution algorithm using k -Nearest Neighbour predictor,” Expert Systems With Applications, vol. 38, no. 4, pp. 4254–4258, 2011.CrossRefGoogle Scholar
  6. 6. A. Diedrich, J. Eickmeyer, P. Li, T. Hoppe, M. Fuchs, and O. Niggemann, “Universal Process Optimization Assistant for Medium-sized Manufacturing Enterprises as Selflearning Expert System,” 2017.Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  • Katharina Giese
    • 1
  • Jens Eickmeyer
    • 1
  • Oliver Niggemann
    • 1
    • 2
  1. 1.Fraunhofer IOSB-INALemgoDeutschland
  2. 2.Institute Industrial IT (inIT)LemgoDeutschland

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