On the Self-organizing Migrating Algorithm Comparison by Means of Centrality Measures

  • Lukas TomaszekEmail author
  • Patrik Lycka
  • Ivan Zelinka
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 554)


In this article we continue in our research which combines three different areas - swarm and evolutionary algorithms, networks and coupled map lattices control. Main aim of this article is to compare networks obtained from best and worst self-organizing migrating algorithm runs. All experiments were done on well known CEC 2014 benchmark functions. For each selected function we picked 30 best and 30 worst runs, converted each run into a network, counted selected properties and compared the results. All obtained results are reported in this article.


Self-organizing migrating algorithm Networks Centrality measure 



The following grants are acknowledged for the financial support provided for this research by Grant of SGS No. 2018/177, VSB-Technical University of Ostrava and under the support of NAVY and MERLIN research lab.


  1. 1.
    Bagler, G.: Analysis of the airport network of India as a complex weighted network. Phys. A: Stat. Mech. Appl. 387(12), 2972–2980 (2008)CrossRefGoogle Scholar
  2. 2.
    Barabási, A.L., Albert, R., Jeong, H.: Scale-free characteristics of random networks: the topology of the world-wide web. Phys. A: Stat. Mech. Appl. 281(1–4), 69–77 (2000)CrossRefGoogle Scholar
  3. 3.
    Barrat, A., Barthelemy, M., Pastor-Satorras, R., Vespignani, A.: The architecture of complex weighted networks. Proc. Nat. Acad. Sci. U.S.A. 101(11), 3747–3752 (2004)CrossRefGoogle Scholar
  4. 4.
    Barrat, A., Barthelemy, M., Vespignani, A.: The architecture of complex weighted networks: measurements and models. In: Large Scale Structure and Dynamics of Complex Networks: From Information Technology to Finance and Natural Science, pp. 67–92. World Scientific (2007)Google Scholar
  5. 5.
    Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.U.: Complex networks: structure and dynamics. Phys. Rep. 424(4–5), 175–308 (2006)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Brandes, U.: A faster algorithm for betweenness centrality. J. Math. Sociol. 25(2), 163–177 (2001)CrossRefGoogle Scholar
  7. 7.
    Davendra, D., Zelinka, I., et al.: Self-organizing migrating algorithm. In: New Optimization Techniques in Engineering (2016)Google Scholar
  8. 8.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1(1), 269–271 (1959)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006)CrossRefGoogle Scholar
  10. 10.
    Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1978)CrossRefGoogle Scholar
  11. 11.
    Krömer, P., Kudělka, M., Senkerik, R., Pluhacek, M.: Differential evolution with preferential interaction network. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 1916–1923. IEEE (2017)Google Scholar
  12. 12.
    Liang, J., Qu, B., Suganthan, P.: Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore (2013)Google Scholar
  13. 13.
    Newman, M.: Networks. Oxford University Press, Oxford (2018)CrossRefGoogle Scholar
  14. 14.
    Newman, M.E.: Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Phys. Rev. E 64(1), 016132 (2001)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Newman, M.E.: Analysis of weighted networks. Phys. Rev. E 70(5), 056131 (2004)CrossRefGoogle Scholar
  16. 16.
    O’Madadhain, J., Fisher, D., Smyth, P., White, S., Boey, Y.B.: Analysis and visualization of network data using jung. J. Stat. Softw. 10(2), 1–35 (2005)Google Scholar
  17. 17.
    Opsahl, T., Agneessens, F., Skvoretz, J.: Node centrality in weighted networks: generalizing degree and shortest paths. Soc. Netw. 32(3), 245–251 (2010)CrossRefGoogle Scholar
  18. 18.
    Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3), 1059–1069 (2010)CrossRefGoogle Scholar
  19. 19.
    Scott, J.: Social Network Analysis. Sage, Thousand Oaks (2017)Google Scholar
  20. 20.
    Soh, H., Lim, S., Zhang, T., Fu, X., Lee, G.K.K., Hung, T.G.G., Di, P., Prakasam, S., Wong, L.: Weighted complex network analysis of travel routes on the singapore public transportation system. Phys. A: Stat. Mech. Appl. 389(24), 5852–5863 (2010)CrossRefGoogle Scholar
  21. 21.
    Tomaszek, L., Zelinka, I.: On performance improvement of the soma swarm based algorithm and its complex network duality. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 4494–4500. IEEE (2016)Google Scholar
  22. 22.
    Tomaszek, L., Zelinka, I.: On static control of swarm systems. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–7. IEEE (2017)Google Scholar
  23. 23.
    Tomaszek, L., Zelinka, I.: Conversion of soma algorithm into complex networks. In: Evolutionary Algorithms, Swarm Dynamics and Complex Networks, pp. 101–114. Springer (2018)Google Scholar
  24. 24.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications, vol. 8. Cambridge University Press, Cambridge (1994)CrossRefGoogle Scholar
  25. 25.
    Zelinka, I.: Investigation on evolutionary deterministic chaos control-extended study. Heuristica 1000, 2 (2005)Google Scholar
  26. 26.
    Zelinka, I.: SOMA–self-organizing migrating algorithm. In: Self-Organizing Migrating Algorithm, pp. 3–49. Springer (2016)Google Scholar
  27. 27.
    Zelinka, I.: On mutual relations amongst evolutionary algorithm dynamics and its hidden complex network structures: an overview and recent advances. In: Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications, pp. 215–239. IGI Global (2017)Google Scholar
  28. 28.
    Zelinka, I., Senkerik, R., Navratil, E.: Investigation on evolutionary optimization of chaos control. Chaos Solitons Fractals 40(1), 111–129 (2009)CrossRefGoogle Scholar
  29. 29.
    Zelinka, I., Tomaszek, L., Kojecky, L.: On evolutionary dynamics modeled by ant algorithm. In: 2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS), pp. 193–198. IEEE (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.VSB-TU OstravaOstravaCzech Republic

Personalised recommendations