Structuring the Model of Complex System Using Parallel Computing Techniques

  • Jan Nikodem
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9520)


The contribution of this paper is to present a results of applications of set theory and relations in modeling a complex distributed systems, based on parallel computing platform. The advantages of using the set theory are: the possibility of a formal examination of the local problems, and the possibility to organize individuals as elements of the considered classes, defined globally. To govern the collective behavior we propose three key relations and mappings determined taxonomic order on them. That can insulate us from reductionism and single-cause thinking, as people deal with complexity before. On three examples, we show how take advantage of the new parallel programming tools to obtain more effective multiple inputs in parallel way, than assigning sequentially single causes for any outputs.


Modeling of complex system Parallel computing Relations 


  1. 1.
    Arsene, C., Bargiela, A., Al-Dabass, D.: Simulation of network systems based on loop flows algorithms. Int. J. Simul. Syst. Sci. Technol. 5(1–2), 61–72 (2004)Google Scholar
  2. 2.
    Box, G.E.P., Draper, N.R.: Empirical Model Building and Response Surfaces. Wiley, New York (1987)zbMATHGoogle Scholar
  3. 3.
    Corning P.A.: Synergy and self-organization in the evolution of complex systems. Syst. Res. 12(2), 89–121 (1995). doi: 10.1002/sres.3850120204 (Wiley)
  4. 4.
    Kuratowski, K., Mostowski, M.: Set Theory, with introduction to descriptive set theory. In: Studies in Logic and the Foundations of Mathematics, vol. 86. PWN-Warsaw, North- Holland- Amsterdam, New York, Oxford (1976)Google Scholar
  5. 5.
    Lloyd, S.: Measures of complexity: a nonexhaustive list. IEEE Control Sys. Mag. 21(4), 7–8 (2001). doi: 10.1109/MCS.2001.939938
  6. 6.
    Marques, J., Cunha, M.C., Sousa, J., Savic, D.: Robust optimization methodologies for water supply systems design. Drinking Water Eng. Sci. 5(1), 31–37 (2012). doi: 10.5194/dwes-5-31-2012
  7. 7.
    Nikodem, J., Klempous, R.: Smart water distribution system, cloud computing. In: ICIT 2013, The 6th International Conference on Information Technology, Amman/Jordan, Al-Dahoud. Al-Zaytoonah University of Jordan, Amman (2013)Google Scholar
  8. 8.
    Nikodem, J.: Modelling of collective animal behavior using relations and set theory. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST. LNCS, vol. 8111, pp. 110–117. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  9. 9.
    Seiffertt, J., Wunsch, D.C.: Introduction. In: Seiffertt, J., Wunsch, D.C. (eds.) Unified Computational Intell. for Complex Sys. ALO, vol. 6, pp. 1–17. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  10. 10.
    Parrish, J.K., Viscido, S.V., Grunbaum, D.: Selforganized fish schools: an examination of emergent properties. Biol. Bull. 202, 296–305 (2002)CrossRefGoogle Scholar
  11. 11.
    Reynolds, C.W.: Flocks, herds, and schools: a distributed behavioral model. In SIGGRAPH 1987 Conference Proceedings, Computer Graphics, vol. 21, no. 4, pp. 25–34 (1987)Google Scholar
  12. 12.
    Siew, C., Tanyimboh, T.: Augmented gradient method for head dependent modeling of water distribution networks. World Environmental and Water Resources Congress (2009). doi: 10.1061/9780784410363
  13. 13.
    Speakman, J.R., Banks, D.: The function of flight formations in Greylag Geese Anser anser; energy saving or orientation? Department of Zoology, University of Aberdeen, Aberdeen AB24 2TZ. Scotland, UK, IBIS 140(2), 280–287 (1998). doi: 10.1111/j.1474-919X.1998.tb04390.x
  14. 14.
    Sumpter, D.J.T.: The principles of collective animal behaviour. Phil. Trans. R. Soc. B 361, 5–22 (2006). doi: 10.1098/rstb.2005.1733

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer EngineeringWrocław University of TechnologyWrocławPoland

Personalised recommendations