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On Static Control of the Differential Evolution

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International Conference on Intelligent Computing and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 846))

Abstract

In this article, we review how to capture the swarm and evolutionary algorithm dynamic into a network, and we show how we can cover the dynamical network into a coupled map lattices. The main part of this article focuses on the control of the algorithm via the coupled map lattices. Mainly, we concentrate on the statical control and the differential evolution algorithm. In the experimental part, we show that we can successfully lower or raise the control property. All the experiments are done on well-known CEC 2015 benchmark functions.

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References

  1. Albert-Laszlo Barabasi, Reka Albert, and Hawoong Jeong. Scale-free characteristics of random networks: the topology of the world wide web. Physica A: statistical mechanics and its applications, 281(1):69–77, 2000.

    Google Scholar 

  2. Alain Barrat, Marc Barthelemy, Romualdo Pastor-Satorras, and Alessandro Vespignani. The architecture of complex weighted networks. Proceedings of the National Academy of Sciences of the United States of America, 101(11):3747–3752, 2004.

    Google Scholar 

  3. Stefano Boccaletti, Vito Latora, Yamir Moreno, Martin Chavez, and DU Hwang. Complex networks: Structure and dynamics. Physics reports, 424(4):175–308, 2006.

    Google Scholar 

  4. Q Chen, B Liu, Q Zhang, JJ Liang, PN Suganthan, and BY Qu. Problem definition and evaluation criteria for cec 2015 special session and competition on bound constrained single-objective computationally expensive numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, China and Nanyang Technological University, Singapore, Tech. Rep, 2014.

    Google Scholar 

  5. Swagatam Das, Sankha Subhra Mullick, and Ponnuthurai N Suganthan. Recent advances in differential evolution–an updated survey. Swarm and Evolutionary Computation, 27:1–30, 2016.

    Google Scholar 

  6. Marco Dorigo, Mauro Birattari, and Thomas Stutzle. Ant colony optimization. IEEE computational intelligence magazine, 1(4):28–39, 2006.

    Google Scholar 

  7. Kunihiko Kaneko. Coupled map lattice. InChaos, Order, and Patterns, pages 237–247. Springer, 1991.

    Google Scholar 

  8. Pavel Kromer, Miloš Kudělka, Roman Senkerik, and Michal Pluhacek. Differential evolution with preferential interaction network. In Evolutionary Computation (CEC), 2017 IEEE Congress on, pages 1916–1923. IEEE, 2017.

    Google Scholar 

  9. Mark Newman. Networks: an introduction. Oxford university press, 2010.

    Google Scholar 

  10. Mark EJ Newman. Analysis of weighted networks. Physical review E, 70(5):056131, 2004.

    Google Scholar 

  11. Tore Opsahl, Filip Agneessens, and John Skvoretz. Node centrality in weighted networks: Generalizing degree and shortest paths. Social networks, 32(3):245–251, 2010.

    Google Scholar 

  12. Mikail Rubinov and Olaf Sporns. Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 52(3):1059–1069, 2010.

    Google Scholar 

  13. Eckehard Scholl and Heinz Georg Schuster. Handbook of chaos control. John Wiley & Sons, 2008.8.

    Google Scholar 

  14. John Scott. Social network analysis. Sage, 2017.

    Google Scholar 

  15. Rainer Storn and Kenneth Price. Differential evolution a simple and efficient adaptive scheme for global optimization over continuous spaces. international computer science institute, berkeley. Berkeley, CA, 1995.

    Google Scholar 

  16. Rainer Storn and Kenneth Price. Differential evolution a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4):341–359, 1997.

    Google Scholar 

  17. Lukas Tomaszek and Ivan Zelinka. On performance improvement of the soma swarm based algorithm and its complex network duality. In Evolutionary Computation (CEC), 2016 IEEE Congress on, pages 4494–4500. IEEE, 2016.

    Google Scholar 

  18. Lukáš Tomaszek and Ivan Zelinka. Conversion of soma algorithm into complex networks. In Evolutionary Algorithms, Swarm Dynamics and Complex Networks, pages 101–114. Springer, 2018.

    Google Scholar 

  19. Stanley Wasserman and Katherine Faust. Social network analysis: Methods and applications, volume 8. Cambridge university press, 1994.

    Google Scholar 

  20. Ivan Zelinka. Investigation on evolutionary deterministic chaos control–extended study. Heuristica, 1000:2, 2005.

    Google Scholar 

  21. Ivan Zelinka. On mutual relations amongst evolutionary algorithm dynamics and its hidden complex network structures: An overview and recent advances. Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications: Concepts, Methodologies, Tools, and Applications, page 215,2016.

    Google Scholar 

  22. Ivan Zelinka, Roman Senkerik, and Eduard Navratil. Investigation on evolutionary optimization of chaos control. Chaos, Solitons & Fractals,40(1):111–129, 2009.

    Google Scholar 

  23. Ivan Zelinka, Lukas Tomaszek, and LumirKojecky. On evolutionary dynamics modeled by ant algorithm. In Intelligent Networking and Collaborative Systems (INCoS), 2016 International Conference on, pages 193–198. IEEE, 2016.

    Google Scholar 

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Acknowledgements

The following grants are acknowledged for the financial support provided for this research: Grant Agency of the Czech Republic—GACR P103/15/06700S and by Grant of SGS No. SP2017/134, VSB Technical University of Ostrava.

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Correspondence to Ivan Zelinka .

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Tomaszek, L., Zelinka, I. (2019). On Static Control of the Differential Evolution. In: Bhaskar, M., Dash, S., Das, S., Panigrahi, B. (eds) International Conference on Intelligent Computing and Applications. Advances in Intelligent Systems and Computing, vol 846. Springer, Singapore. https://doi.org/10.1007/978-981-13-2182-5_2

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