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The Effect of Swapping Vectors During Mutation in Differential Evolution

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8947))

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Abstract

This paper considers the effect of swapping vectors during mutation, which are used for mutant vector construction. In the classic/canonical differential evolution three mutually different vector are picked from the population, where one represents the base vector, and the difference of the remaining two represents the difference vector. Motivated by the fact that there is no selection pressure in selecting the base vector, the effect of setting the best one of the selected three as the base vector is investigated. This way, a corresponding selection pressure is achieved and the exploration of the search space is directed more towards better solutions. Additionally, the order of the vectors used for generating the difference vector is considered as well. The experimental analysis conducted on a fair number of standard benchmark functions of different dimensionalities and properties indicates that the aforementioned approach performs competitively or better compared to the canonical differential evolution.

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References

  1. Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 342–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  2. Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Springer-Verlag New York Inc., Secaucus (2005)

    MATH  Google Scholar 

  3. Quaranta, G., Monti, G., Marano, G.C.: Parameters identification of Van der Pol-Duffing oscillators via particle swarm optimization and differential evolution. Mech. Syst. Signal Process. 24, 2076–2095 (2010)

    Article  Google Scholar 

  4. Martinović, G., Bajer, D.: Data clustering with differential evolution incorporating macromutations. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds.) SEMCCO 2013, Part I. LNCS, vol. 8297, pp. 158–169. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  5. Martinović, G., Bajer, D., Zorić, B.: A differential evolution approach to dimensionality reduction for classification needs. Int. J. Appl. Math. Comput. Sci. 24, 111–122 (2014)

    MathSciNet  Google Scholar 

  6. Salman, A.A., Ahmad, I., Omran, M.G.H., Mohammad, M.Gh.: Frequency assignment problem in satellite communications using differential evolution. Comput. Operat. Res. 37, 2152–2163 (2010)

    Google Scholar 

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

    Article  Google Scholar 

  8. Das, S., Konar, A., Chakraborty, U.K.: Two improved differential evolution schemes for faster global search. In: 7th Annual Conference on Genetic and Evolutionary Computation, pp. 991–998. ACM, New York (2005)

    Google Scholar 

  9. Zhan, Z.-H., Zhang, J.: Enhance differential evolution with random walk. In: 14th International Conference on Genetic and Evolutionary Computation Conference Companion, pp. 1513–1514. ACM, New York (2012)

    Google Scholar 

  10. Liu, Y., Sun, F.: A fast differential evolution algorithm using k-nearest neighbour predictor. Expert Syst. Appl. 28, 4254–4258 (2011)

    Article  Google Scholar 

  11. Huang, Z., Chen, Y.: An improved differential evolution algorithm based on adaptive parameter. J. Control Sci. Eng. 2013, 5 (2013)

    Google Scholar 

  12. Li, R., Xu, L., Shi, X.-W., Zhang, N., Lv, Z.-Q.: Improved differential evolution strategy for antenna array pattern synthesis problems. Prog. Electromagnet. Res. 113, 429–441 (2011)

    Article  Google Scholar 

  13. Kaelo, P., Ali, M.M.: A numerical study of some modified differential evolution algorithms. Eur. J. Op. Res. 169, 1176–1184 (2006)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  15. Blickle, T., Thiele, L.: A comparison of selection schemes used in evolutionary algorithms. Evol. Comput. 4, 361–394 (1996)

    Article  MATH  Google Scholar 

  16. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39, 459–471 (2007)

    Article  MathSciNet  Google Scholar 

  17. Noman, N., Bollegala, D., Iba, H.: An adaptive differential evolution algorithm. In: 2011 IEEE Congress on Evolutionary Computation, pp. 2229–2236 (2011)

    Google Scholar 

  18. Bansal, J.C., Singh, P.K., Saraswat, M., Verma, A., Jadon, S.S., Abraham, A.: Inertia weight strategies in particle swarm optimization. In: Third World Congress on Nature and Biologically Inspired Computing, pp. 633–640. IEEE (2011)

    Google Scholar 

  19. Yao, S., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Trans. Evol. Comput. 3, 82–102 (1999)

    Article  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  21. Ji, M., Klinowski, J.: Taboo evolutionary programming: a new method of global optimization. Proc. R. Soc. A 462, 3613–3627 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  22. Jamil, M., Yang, X.-S.: A literature survey of benchmark functions for global optimisation problems. Int. J. Math. Model. Numer. Optim. 4, 150–194 (2013)

    MATH  Google Scholar 

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Acknowledgments

This work was supported by research project grant No. 165-0362980-2002 from the Ministry of Science, Education and Sports of the Republic of Croatia. The authors would like to thank the anonymous reviewers for their useful comments that helped improve the paper.

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Authors and Affiliations

  1. Faculty of Electrical Engineering, J. J. Strossmayer University of Osijek, Kneza Trpimira 2b, 31000, Osijek, Croatia

    Goran Martinović & Dražen Bajer

Authors
  1. Goran Martinović
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  2. Dražen Bajer
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Corresponding author

Correspondence to Goran Martinović .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, IIT, New Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India

    Swagatam Das

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Martinović, G., Bajer, D. (2015). The Effect of Swapping Vectors During Mutation in Differential Evolution. In: Panigrahi, B., Suganthan, P., Das, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science(), vol 8947. Springer, Cham. https://doi.org/10.1007/978-3-319-20294-5_47

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  • DOI: https://doi.org/10.1007/978-3-319-20294-5_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20293-8

  • Online ISBN: 978-3-319-20294-5

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