Skip to main content
SpringerLink
Log in

Investigation of Mutation Strategies in Differential Evolution for Solving Global Optimization Problems

  • Conference paper
Artificial Intelligence and Soft Computing (ICAISC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8467))

Included in the following conference series:

Abstract

Differential evolution (DE) is one competitive form of evolutionary algorithms. It heavily relies on mutating solutions using scaled differences of randomly selected individuals from the population to create new solutions. The choice of a proper mutation strategy is important for the success of an DE algorithm. This paper presents an empirical investigation to examine and compare the different mutation strategies for global optimization problems. Both solution quality and computational expense of DE variants were evaluated with experiments conducted on a set of benchmark problems. The results of such comparative study would offer valuable insight and information to develop optimal or adaptive mutation strategies for future DE researches and applications.

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Herrera, F., Lozano, M., Verdegay, J.: Tackling real-coded genetic algorithms: Operators and tools for the behavioral analysis. Artificial Intelligence Review 12, 265–319 (1998)

    Article  MATH  Google Scholar 

  2. Beyer, H., Schwefel, H.: Evolution strategies: A comprehensive introduction. Natural Computing 1, 3–52 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  3. Lee, C., Yao, X.: Evolutionary programming using mutations based on the levy probability distribution. IEEE Transactions on Evolutionary Computation 8, 1–13 (2004)

    Article  Google Scholar 

  4. Xiong, N., Leon, M.: Principles and state-of-the-art of engineering optimization techniques. In: Proc. The Seventh International Conference on Advanced Engineering Computing and Applications in Sciences, ADVCOMP 2013, Porto, Portugal, pp. 36–42 (2013)

    Google Scholar 

  5. Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4), 341–359 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  6. Price, K., Storn, Lampinen, J.: Differential evolution a practical approach to global optimization. Springer Natural Computing Series (2005)

    Google Scholar 

  7. Kumar, P., Pant, M.: Enhanced mutation strategy for differential evolution. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1–6 (2012)

    Google Scholar 

  8. Qu, B., Suganthan, P., Liang, J.: Differential evolution with neighborhood mutation for multimodal optimization. IEEE Transactions on Evolutionary Computation 16, 601–614 (2012)

    Article  Google Scholar 

  9. Noman, N., Iba, N.: Enhancing differential evolution performance with local search for high dimensional function optimization. In: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, GECCO 2005, pp. 967–974 (2005)

    Google Scholar 

  10. Dai, Z., Zhou, A.: A differential evolution with an orthogonal local search. In: Proc. 2013 IEEE Congress on Evolutionary Computation (CEC), Cancun, Mexico, pp. 2329–2336 (2013)

    Google Scholar 

  11. Poikolainen, I., Neri, F.: Differential evolution with concurrent fitness based local search. In: Proc. 2013 IEEE Congress on Evolutionary Computation (CEC), Cancun, Mexico, pp. 384–391 (2013)

    Google Scholar 

  12. Leon, M., Xiong, N.: Using random local search helps in avoiding local optimum in differential evolution. In: Proc. Artificial Intelligence and Applications, AIA 2014, Innsbruck, Austria, pp. 413–420 (2014)

    Google Scholar 

  13. Xu, H., Wen, J.: Differential evolution algorithm for the optimization of the vehicle routing problem in logistics. In: Proc. 2012 Eighth International Conference on Computational Intelligence and Security (CIS), Guangzhou, China, pp. 48–51 (2012)

    Google Scholar 

  14. Gong, W., Cai, Z.: Differential evolution with ranking-based mutation operators. IEEE Transactions on Cybernetics PP, 1–16 (2013)

    Google Scholar 

  15. Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. Proc. IEEE Transactions on Evolutionary Computation 3, 82–102 (1999)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mälardalen University, Västerås, Sweden

    Miguel Leon & Ning Xiong

Authors
  1. Miguel Leon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ning Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Computer Science Division, Department of Electrical Engineering and Computer Sciences, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Leon, M., Xiong, N. (2014). Investigation of Mutation Strategies in Differential Evolution for Solving Global Optimization Problems. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8467. Springer, Cham. https://doi.org/10.1007/978-3-319-07173-2_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-07173-2_32

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07172-5

  • Online ISBN: 978-3-319-07173-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation