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On the Randomization of Indices Selection for Differential Evolution

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Artificial Intelligence Trends in Intelligent Systems (CSOC 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 573))

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Abstract

This research deals with the hybridization of two softcomputing fields, which are the chaos theory and evolutionary algorithms. This paper investigates the utilization of the two-dimensional discrete chaotic systems, which are Burgers and Lozi maps, as the chaotic pseudo random number generators (CPRNGs) embedded into the selected heuristics, which is differential evolution algorithm (DE). Through the utilization of either chaotic systems or identical identified pseudo random number distribution, it is possible to fully keep or remove the hidden complex chaotic dynamics from the generated pseudo random data series. Experiments are focused on the extended investigation, whether the different randomization types with different pseudo random numbers distribution or hidden complex chaotic dynamics providing the unique sequencing are more beneficial to the heuristic performance. This research utilizes set of 4 selected benchmark functions, and totally four different randomizations; further results are compared against canonical DE.

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References

  1. Price, K.V., An introduction to differential evolution. In: Corne, D., Dorigo, M., Glover, F., (eds.) New Ideas in Optimization, pp. 79–108. McGraw-Hill Ltd., London (1999)

    Google Scholar 

  2. Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V.: Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10(6), 646–657 (2006)

    Article  Google Scholar 

  3. Mallipeddi, R., Suganthan, P.N., Pan, Q.K., Tasgetiren, M.F.: Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl. Soft Comput. 11(2), 1679–1696 (2011)

    Article  Google Scholar 

  4. Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)

    Article  Google Scholar 

  5. Das, S., Mullick, S.S., Suganthan, P.N.: Recent advances in differential evolution – an updated survey. Swarm Evol. Comput. 27, 1–30 (2016)

    Article  Google Scholar 

  6. Neri, F., Tirronen, V.: Recent advances in differential evolution: a survey and experimental analysis. Artif. Intell. Rev. 33(1–2), 61–106 (2010)

    Article  Google Scholar 

  7. Weber, M., Neri, F., Tirronen, V.: A study on scale factor in distributed differential evolution. Inf. Sci. 181(12), 2488–2511 (2011)

    Article  Google Scholar 

  8. Neri, F., Iacca, G., Mininno, E.: Disturbed exploitation compact differential evolution for limited memory optimization problems. Inf. Sci. 181(12), 2469–2487 (2011)

    Article  MathSciNet  Google Scholar 

  9. Iacca, G., Caraffini, F., Neri, F.: Compact differential evolution light: high performance despite limited memory requirement and modest computational overhead. J. Comput. Sci. Technol. 27(5), 1056–1076 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  10. Zamuda, A., Brest, J.: Self-adaptive control parameters’ randomization frequency and propagations in differential evolution. Swarm Evol. Comput. 25, 72–99 (2015)

    Article  Google Scholar 

  11. Caponetto, R., Fortuna, L., Fazzino, S., Xibilia, M.G.: Chaotic sequences to improve the performance of evolutionary algorithms. IEEE Trans. Evol. Comput. 7(3), 289–304 (2003)

    Article  Google Scholar 

  12. Davendra, D., Zelinka, I., Senkerik, R.: Chaos driven evolutionary algorithms for the task of PID control. Comput. Math. Appl. 60(4), 1088–1104 (2010)

    Article  MATH  Google Scholar 

  13. Zelinka, I.: SOMA — self-organizing migrating algorithm. In: Zelinka, I. (ed.) New Optimization Techniques in Engineering. Studies in Fuzziness and Soft Computing, vol. 141, pp. 167–217. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  14. dos Santos Coelho, L., Mariani, V.C.: A novel chaotic particle swarm optimization approach using Hénon map and implicit filtering local search for economic load dispatch. Chaos, Solitons Fractals 39(2), 510–518 (2009)

    Article  Google Scholar 

  15. Pluhacek, M., Senkerik, R., Davendra, D., Kominkova Oplatkova, Z., Zelinka, I.: On the behavior and performance of chaos driven PSO algorithm with inertia weight. Comput. Math. Appl. 66(2), 122–134 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  16. Pluhacek, M., Senkerik, R., Zelinka, I., Davendra, D.: Chaos PSO algorithm driven alternately by two different chaotic maps – an initial study. In: 2013 IEEE Congress on Evolutionary Computation (CEC), 20–23 June 2013, pp. 2444–2449 (2013)

    Google Scholar 

  17. Pluhacek, M., Senkerik, R., Davendra, D.: Chaos particle swarm optimization with Eensemble of chaotic systems. Swarm Evol. Comput. 25, 29–35 (2015)

    Article  Google Scholar 

  18. Metlicka, M., Davendra, D.: Chaos driven discrete artificial bee algorithm for location and assignment optimisation problems. Swarm Evol. Comput. 25, 15–28 (2015)

    Article  Google Scholar 

  19. dos Santos Coelho, L., Ayala, H.V.H., Mariani, V.C.: A self-adaptive chaotic differential evolution algorithm using gamma distribution for unconstrained global optimization. Appl. Math. Comput. 234, 452–459 (2014)

    MathSciNet  MATH  Google Scholar 

  20. Senkerik, R., Pluhacek, M., Zelinka, I., Davendra, D., Janostik, J.: Preliminary study on the randomization and sequencing for the chaos embedded heuristic. In: Abraham, A., Wegrzyn-Wolska, K., Hassanien, A.E., Snasel, V., Alimi, A.M. (eds.) Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015. AISC, vol. 427, pp. 591–601. Springer, Cham (2016). doi:10.1007/978-3-319-29504-6_55

    Chapter  Google Scholar 

  21. Zelinka, I.: A survey on evolutionary algorithms dynamics and its complexity – mutual relations, past, present and future. Swarm Evol. Comput. 25, 2–14 (2015)

    Article  Google Scholar 

  22. Das, S., Abraham, A., Chakraborty, U.K., Konar, A.: Differential evolution using a neighborhood-based mutation operator. IEEE Trans. Evol. Comput. 13(3), 526–553 (2009)

    Article  Google Scholar 

  23. Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution – A Practical Approach to Global Optimization. Natural Computing Series. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  24. Sprott, J.C.: Chaos and Time-Series Analysis. Oxford University Press, New York (2003)

    MATH  Google Scholar 

  25. Senkerik, R., Pluhacek, M., Kominkova Oplatkova, Z., Davendra, D.: On the parameter settings for the chaotic dynamics embedded differential evolution. In: 2015 IEEE Congress on Evolutionary Computation (CEC), 25–28 May 2015, pp. 1410–1417 (2015)

    Google Scholar 

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Acknowledgements

This work was supported by Grant Agency of the Czech Republic - GACR P103/15/06700S, further by financial support of research project NPU I No. MSMT-7778/2014 by the Ministry of Education of the Czech Republic and also by the European Regional Development Fund under the Project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089, and by Internal Grant Agency of Tomas Bata University under the projects No. IGA/CEBIA-Tech/2017/004.

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Authors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam T.G. Masaryka 5555, 760 01, Zlin, Czech Republic

    Roman Senkerik, Michal Pluhacek, Adam Viktorin & Tomas Kadavy

Authors
  1. Roman Senkerik
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  2. Michal Pluhacek
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  3. Adam Viktorin
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  4. Tomas Kadavy
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Corresponding author

Correspondence to Roman Senkerik .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlin, Czech Republic

    Radek Silhavy

  2. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlin, Czech Republic

    Roman Senkerik

  3. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlin, Czech Republic

    Zuzana Kominkova Oplatkova

  4. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlin, Czech Republic

    Zdenka Prokopova

  5. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlin, Czech Republic

    Petr Silhavy

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Senkerik, R., Pluhacek, M., Viktorin, A., Kadavy, T. (2017). On the Randomization of Indices Selection for Differential Evolution. In: Silhavy, R., Senkerik, R., Kominkova Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds) Artificial Intelligence Trends in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-319-57261-1_53

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  • DOI: https://doi.org/10.1007/978-3-319-57261-1_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57260-4

  • Online ISBN: 978-3-319-57261-1

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