1. Proceedings
  2. I3M
  3. 2019
  4. EMSS
  5. 10.46354/i3m.2019.emss.047

Evaluation of a Self-Organizing Migrating Algorithm applied to discrete event simulation optimization

  • a��Pavel Raska  ,
  • b Zdenek Ulrych  
  • a,b Department of Industrial Engineering - Faculty of Mechanical Engineering, University of West Bohemia, Univerzitni 22, 306 14 Pilsen, Czech Republic
Cite as
Raska P., Ulrych Z. (2019). Evaluation of a Self-Organizing Migrating Algorithm applied to discrete event simulation optimization. Proceedings of the 31st European Modeling & Simulation Symposium (EMSS 2019), pp. 332-341. DOI: https://doi.org/10.46354/i3m.2019.emss.047.

Abstract

The paper deals with testing and evaluation of a modified Self-Organizing Migrating Algorithm (SOMA) applied to a discrete event simulation model reflecting the supply of production lines using automated guided vehicles. The SOMA heuristic optimization method is derived from the Differential Evolution method. We test all the SOMA strategies under the same conditions of the simulation experiments – the same termination criteria, number of repetitions in the optimization experiments, and the same setting of the basic parameters of the SOMA. We propose a methodology using different evaluation criteria to analyse the different SOMA strategies behaviour of finding the optimum of an objective function specified for each discrete event simulation model.

References

  1. Bučková M., Krajčovič M., Edl M., 2017. Computer Simulation and Optimization of Transport Distances of Order Picking Processes. Procedia Engineering, 192, 69-74.
  2. Borda, M., 2011. Fundamentals in Information Theory and Coding. Berlin: Springer Berlin Heidelberg.
  3. Elsayed, S. M., Ruhul A. S., Daryl L. E., 2013. An Improved Self-Adaptive Differential Evolution Algorithm for Optimization Problems. IEEE Transactions on Industrial Informatics, 9 (1), 89-99.
  4. Koblasa, F., Vavrousek, M., Manlig, F., 2017. Threedimensional Bin Packing Problem with heterogeneous batch constraints. In: P. Prazak, ed. Mathematical Methods in Economics, 330–335.
  5. Li Y. L., Zhan Z. H., Gong Y. J., Chen W. N., Zhang J., Li, Y. 2015. Differential Evolution with an Evolution Path: A DEEP Evolutionary Algorithm. IEEE Transactions on Cybernetics, 45(9), 1798–1810.
  6. Pohlheim, H., 2006. GEATbx: Example Functions. Available from: http://www.geatbx.com/docu/fcnindex-01.html#P204_10395 [accessed 20 November 2011]
  7. Raska, P., and Ulrych, Z., 2014. Hierarchical approach to developing a logistic discrete event simulation model using Automated Guided Vehicles. 26th European Modeling and Simulation Symposium, pp. 205-211. Bordeaux (France).
  8. Volna, E., 2012. Evolutionary Algorithms and Neural Networks (in Czech language: Evolucni algoritmy a neuronove site). University of Ostrava. Available from: http://www1.osu.cz/~volna/Evolucni_algoritmy_a_neuronove_site.pdf [accessed 5 March 2016]
  9. Weise, T., 2009. E-Book "Global Optimization Algorithms - Theory and Application" 2nd Edition. Available from: http://www.itweise.de/projects/book.pdf [accessed 2 February 2011]
  10. Zelinka, I., 2016. SOMA—Self-organizing Migrating Algorithm. In: Studies in Computational Intelligence. Springer Verlag, 3–49.
  11. Zelinka, I., Snasel, V. and Abraham, A., 2013. Handbook of Optimization: From Classical to Modern Approach. Intelligent Systems Reference Library, Berlin: Springer.
  12. Zelinka, I., Vybíral J., 2005. SOMA - Source Codes. Tomas Bata University in Zlín. Available from: http://www.ft.utb.cz/people/zelinka/soma/[accessed 6 March 2012]