Skip to main content
SpringerLink
Log in

On the Efficiency of Successful-Parent Selection Framework in the State-of-the-art Differential Evolution Variants

  • Conference paper
  • First Online:
Artificial Intelligence and Soft Computing (ICAISC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10245))

Included in the following conference series:

Abstract

Successful-parent selection (SPS) framework in differential evolution (DE) is studied. Two SPS versions (SPS1 proposed recently in literature and SPS2 newly proposed in this paper) are applied to seven state-of-the-art DE variants. The algorithms are compared experimentally on CEC 2014 test suite used as a benchmark. The application of SPS1 increases the efficiency of two DE algorithms in over \(50\%\) of test problems. An overall comparison shows that the newly proposed SPS2 performs well only in two cases whereas SPS1 outperforms six out of 7 original algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15, 27–54 (2011)

    Google Scholar 

  4. Guo, S.M., Yang, C.C., Hsu, P.H., Tsai, J.S.H.: Improving differential evolution with successful-parent-selecting framework. IEEE Trans. Evol. Comput. 19(5), 717–730 (2015)

    Article  Google Scholar 

  5. Lampinen, J., Zelinka, I.: On stagnation of the differential evolution algorithm. In: Matousek, R. (ed.) 6th International Conference on Soft Computing Mendel 2000, pp. 76–83 (2000)

    Google Scholar 

  6. Liang, J.J., Qu, B.Y., Suganthan, P.N.: Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Tech. rep., Nanyang Technological University, Singapore (2013). http://www.ntu.edu.sg/home/epnsugan/

  7. 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, 1679–1696 (2011)

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Price, K.V., Storn, R., Lampinen, J.: Differential Evolution: A Practical Approach to Global Optimization. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  10. Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(2), 398–417 (2009)

    Article  Google Scholar 

  11. Storn, R., Price, K.V.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  12. Tanabe, R., Fukunaga, A.: Success-history based parameter adaptation for differential evolution. IEEE Congr. Evol. Comput. (CEC) 2013, pp. 71–78, June 2013

    Google Scholar 

  13. Tang, L., Dong, Y., Liu, J.: Differential evolution with an individual-dependent mechanism. IEEE Trans. Evol. Comput. 19(4), 560–574 (2015)

    Article  Google Scholar 

  14. Tušar, T., Filipič, B.: Differential evolution versus genetic algorithms in multiobjective optimization. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 257–271. Springer, Heidelberg (2007). doi:10.1007/978-3-540-70928-2_22

    Chapter  Google Scholar 

  15. Vesterstrom, J., Thomsen, R.: A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: Congress on Evolutionary Computation, CEC 2004, vol. 2, pp. 1980–1987, June 2004

    Google Scholar 

  16. Wang, Y., Cai, Z., Zhang, Q.: Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans. Evol. Comput. 15, 55–66 (2011)

    Article  Google Scholar 

  17. Zhang, J., Sanderson, A.C.: JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13, 945–958 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Ostrava, 30. dubna 22, 70103, Ostrava, Czech Republic

    Petr Bujok

Authors
  1. Petr Bujok
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Petr Bujok .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  4. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Bujok, P. (2017). On the Efficiency of Successful-Parent Selection Framework in the State-of-the-art Differential Evolution Variants. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10245. Springer, Cham. https://doi.org/10.1007/978-3-319-59063-9_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59063-9_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59062-2

  • Online ISBN: 978-3-319-59063-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation