Skip to main content
SpringerLink
Log in

A self-adaptive differential evolution algorithm for continuous optimization problems

  • Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

Abstract

This paper proposes a new self-adaptive differential evolution algorithm (DE) for continuous optimization problems. The proposed self-adaptive differential evolution algorithm extends the concept of the DE/current-to-best/1 mutation strategy to allow the adaptation of the mutation parameters. The control parameters in the mutation operation are gradually self-adapted according to the feedback from the evolutionary search. Moreover, the proposed differential evolution algorithm also consists of a new local search based on the krill herd algorithm. In this study, the proposed algorithm has been evaluated and compared with the traditional DE algorithm and two other adaptive DE algorithms. The experimental results on 21 benchmark problems show that the proposed algorithm is very effective in solving complex optimization problems.

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 (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359

    Article  MathSciNet  MATH  Google Scholar 

  2. Onwubolu G, Davendra D (2006) Scheduling flow shops using differential evolution algorithm. Eur J Oper Res 171:674–692

    Article  MATH  Google Scholar 

  3. Pan Q, Wang L, Gao L, Li WD (2011) An effective hybrid discrete differential evolution algorithm for the flow shop scheduling with intermediate buffers. Inf Sci 181:668–685

    Article  Google Scholar 

  4. Kitayama S, Arakawa M, Yamazaki K (2011) Differential evolution as the global optimization technique and its application to structural optimization. Appl Soft Comput 11:3792–3803

    Article  Google Scholar 

  5. Boussaïd I, Chatterjee A, Siarry P, Ahmed-Nacer M (2011) Two-stage update biogeography-based optimization using differential evolution algorithm (DBBO). Comput Oper Res 38:1188–1198

    Article  MathSciNet  MATH  Google Scholar 

  6. Liao TW (2010) Two hybrid differential evolution algorithms for engineering design optimization. Appl Soft Comput 10:1188–1199

    Article  Google Scholar 

  7. Ali M, Siarry P, Pant M (2012) An efficient differential evolution based algorithm for solving multi-objective optimization problems. Eur J Oper Res 217:404–416

    MathSciNet  MATH  Google Scholar 

  8. Madavan NK (2002) Multiobjective optimization using a Pareto differential evolution approach. In: Proceedings of the 2002 congress on evolutionary computation, vol 2, pp 1145–1150

  9. Robic T, Filipic B (2005) DEMO: differential evolution for multiobjective optimization. In: Proceedings of the 3rd international conference on evolutionary multi-criterion optimization, LNCS, vol 3410, pp 520–533

  10. Piotrowski AP, Napiorkowski JJ, Kiczko A (2012) Differential evolution algorithm with separated groups for multi-dimensional optimization problems. Eur J Oper Res 216:33–46

    Article  MathSciNet  MATH  Google Scholar 

  11. Melo VV, Carosio GLC (2013) Investigating multi-view differential evolution for solving constrained engineering design problems. Expert Syst Appl 40:3370–3377

    Article  Google Scholar 

  12. Asafuddoula M, Ray T, Sarker R (2014) An adaptive hybrid differential evolution algorithm for single objective optimization. Appl Math Comput 231:601–618

    Article  MathSciNet  Google Scholar 

  13. Zhou Y, Li X, Gao L (2013) A differential evolution algorithm with intersect mutation operator. Appl Soft Comput 13:390–401

    Article  Google Scholar 

  14. Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simulat 17:4831–4845

    Article  MathSciNet  MATH  Google Scholar 

  15. Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958

    Article  Google Scholar 

  16. Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417

    Article  Google Scholar 

  17. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computational Intelligence Laboratory, Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, 10520, Thailand

    Duangjai Jitkongchuen & Arit Thammano

Authors
  1. Duangjai Jitkongchuen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arit Thammano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Duangjai Jitkongchuen.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jitkongchuen, D., Thammano, A. A self-adaptive differential evolution algorithm for continuous optimization problems. Artif Life Robotics 19, 201–208 (2014). https://doi.org/10.1007/s10015-014-0155-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-014-0155-z

Keywords

Search

Navigation