Skip to main content
SpringerLink
Log in

A new differential mutation base generator for differential evolution

  • Published:
Journal of Global Optimization Aims and scope Submit manuscript

Abstract

A new differential mutation base strategy for differential evolution (DE), namely best of random, is proposed. The best individual among several randomly chosen individuals is chosen as the differential mutation base while the other worse individuals are donors for vector differences. Hence both good diversity and fast evolution speed can be obtained in DE using the new differential mutation base. A comprehensive comparative study is carried out over a set of benchmark functions. Numerical results show that a better balance of reliability and efficiency can be obtained in differential evolution implementing the new generator of differential mutation base, especially in functions with high dimension.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Storn, R., Price, K.: Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, International Computer Science Institute, Berkley, CA (1995)

  2. Storn R., Price K.V.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)

    Article  Google Scholar 

  3. Price K.V.: An introduction to differential evolution. In: Corn, D., Dorigo, M., Glover, F. (eds) New Ideas in Optimization, chap. 6., pp. 79–108. McGraw-Hill, London (1999)

    Google Scholar 

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

    Google Scholar 

  5. Qing A.: Differential Evolution: Fundamentals and Applications in Engineering. Wiley, New York (2009)

    Google Scholar 

  6. Feoktistov V.: Differential Evolution: In Search of Solutions. Springer, Berlin (2006)

    Google Scholar 

  7. Joshi, R., Sanderson, A.C.: Multisensor fusion and model selection using a minimal representation size framework. In: IEEE/SICE/RSJ International Conference on Multisensor Fusion Integration Intelligent Systems, Washington, DC, December 8–11, 1996, pp. 25–32 (1996)

  8. Storn, R.: On the usage of differential evolution for function optimization. In: North American Fuzzy Information Processing Society Conference, Berkeley, pp. 519–523 (1996)

  9. Qing A.: Electromagnetic inverse scattering of multiple two-dimensional perfectly conducting objects by the differential evolution strategy. IEEE Trans. Antennas Propag. 51(6), 1251–1262 (2003)

    Article  Google Scholar 

  10. Vesterstrøm, J., Thomsen, R.: A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: IEEE Congress Evolutionary Computation, Portland, OR, June 19–23, 2004, vol. 2, pp. 1980–1987 (2004)

  11. Paterlini S., Krink T.: Differential evolution and particle swarm optimisation in partitional clustering. Comput. Stat. Data Anal. 50(5), 1220–1247 (2006)

    Article  Google Scholar 

  12. Wong, K.P., Dong, Z.Y.: Differential evolution, an alternative approach to evolutionary algorithm. In: 13th International Conference Intelligent Systems Application Power Systems. Arlington, VA, Nov. 6–10, pp. 73–83 (2005)

  13. http://www.icsi.berkeley.edu/~storn/code.html

  14. Qing, A.: A parametric study on differential evolution based on benchmark electromagnetic inverse scattering problem. In: IEEE Congress Evolutionary Computation, Singapore, Sept. 25–28, 2007, pp. 1904–1909 (2007)

  15. Qing, A.: A study on base vector for differential evolution. IEEE World Congress Computational Intelligence/2008 IEEE Congress Evolutionary Computation, Hong Kong, June 1–6, 2008, pp. 550–556 (2008)

  16. Qing A.: Dynamic differential evolution strategy and applications in electromagnetic inverse scattering problems. IEEE Trans. Geosci. Remote Sens. 44(1), 116–125 (2006)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 610031, China

    Chuan Lin & Quanyuan Feng

  2. Temasek Laboratories, National University of Singapore, 5A Engineering Dr 1#06-09, Singapore, 117508, Singapore

    Chuan Lin & Anyong Qing

Authors
  1. Chuan Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anyong Qing
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Quanyuan Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chuan Lin.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lin, C., Qing, A. & Feng, Q. A new differential mutation base generator for differential evolution. J Glob Optim 49, 69–90 (2011). https://doi.org/10.1007/s10898-010-9535-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10898-010-9535-7

Keywords

Search

Navigation