Skip to main content
SpringerLink
Log in

A novel mutation operator based on the union of fitness and design spaces information for Differential Evolution

  • Foundations
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Differential Evolution (DE) is one of the most successful and powerful evolutionary algorithms for global optimization problem. The most important operator in this algorithm is mutation operator, in which parents are selected randomly to participate in it. Recently, numerous papers are tried to make this operator more intelligent by selection of parents for mutation intelligently. The intelligent selection for mutation vectors is performed by applying design space (also known as decision space) criterion or fitness space criterion; however, in both cases, half of valuable information of the problem space is disregarded. In this article, a Union Differential Evolution (UDE) is proposed which takes advantage of both design and fitness spaces criteria for intelligent selection of mutation vectors. The experimental analysis on UDE are performed on CEC2005 benchmarks and the results stated that UDE significantly improved the performance of DE in comparison with other methods that only use one criterion for intelligent selection.

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

  • Baatar N, Dianhai Z, Chang-Seop K (2013) An improved differential evolution algorithm adopting \(\lambda \)-best mutation strategy for global optimization of electromagnetic devices. IEEE Trans Magn 2097–2100. doi:10.1109/TMAG.2013.2240284

  • Bäck T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, Oxford

    MATH  Google Scholar 

  • Biswas S, Kundu S, Das S (2014) An Improved parent-centric mutation with normalized neighborhoods for inducing niching behavior in differential evolution. IEEE Trans Cybern 1726–1737. doi:10.1109/TCYB.2013.2292971

  • Boussaïd I, Lepagnot J, Siarry P (2013) A survey on optimization metaheuristics. Inf Sci 82–117. doi:10.1016/j.ins.2013.02.041

  • Brest J, Zamuda A, Bošković B, Greiner S, Žumer V (2008) An analysis of the control parameters’ adaptation in DE. In: Chakraborty U (ed) Advances in differential evolution. Springer, Berlin, pp 89–110

    Chapter  Google Scholar 

  • 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 Evolut Comput 646–657. doi:10.1109/TEVC.2006.872133

  • Das S, Mandal A, Mukherjee R (2014) An adaptive differential evolution algorithm for global optimization in dynamic environments. IEEE Trans Cybern 966–978. doi:10.1109/TCYB.2013.2278188

  • Das S, Mullick SS, Suganthan PN (2016) Recent advances in differential evolution—an updated survey. Swarm Evolut Comput 1–30. doi:10.1016/j.swevo.2016.01.004

  • Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evolut Comput 4–31. doi:10.1109/TEVC.2010.2059031

  • Epitropakis MG, Tasoulis DK, Pavlidis NG, Plagianakos VP, Vrahatis MN (2011) Enhancing differential evolution utilizing proximity-based mutation operators. IEEE Trans Evolut Comput 99–119. doi:10.1109/TEVC.2010.2083670

  • García-Martínez C, Rodríguez F, Lozano M (2011) Role differentiation and malleable mating for differential evolution: an analysis on large-scale optimisation. Soft Comput 2109–2126. doi:10.1007/s00500-010-0641-8

  • Gong W, Cai Z (2013) Differential evolution with ranking-based mutation operators. IEEE Trans Cybern 2066–2081. doi:10.1109/TCYB.2013.2239988

  • Iacca G, Neri F, Caraffini F, Suganthan PN (2014) A differential evolution framework with ensemble of parameters and strategies and pool of local search algorithms. In: Esparcia-Alcázar IA, Mora MA (eds) Applications of evolutionary computation: 17th European conference, EvoApplications 2014, Granada, Spain, April 23–25, 2014, revised selected papers. Springer, Berlin, pp 615–626

  • Jingqiao Z, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evolut Comput 945–958. doi:10.1109/TEVC.2009.2014613

  • Kaelo P, Ali MM (2006) A numerical study of some modified differential evolution algorithms. Eur J Oper Res 1176–1184. doi:10.1016/j.ejor.2004.08.047

  • Liang JJ, Qu BY, Mao XB, Niu B, Wang DY (2014) Differential evolution based on fitness Euclidean-distance ratio for multimodal optimization. Neurocomputing 252–260. doi:10.1016/j.neucom.2013.03.069

  • Mallipeddi R, Suganthan PN (2010) Differential evolution algorithm with ensemble of parameters and mutation and crossover strategies. In: Panigrahi BK, Das S, Suganthan PN, Dash SS (eds) Swarm, evolutionary, and memetic computing: first international conference on swarm, evolutionary, and memetic computing, SEMCCO 2010, Chennai, India, December 16–18, 2010 proceedings. Springer, Berlin, pp 71–78

  • Matej, C, Liu S-H, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv 1–33. doi:10.1145/2480741.2480752

  • Michalewicz Z (1996) Genetic algorithms + data structures = evolution programs, 3rd edn. Springer, Berlin

    Book  MATH  Google Scholar 

  • Mohammadi M, Sharifi Noghabi H, Abed Hodtani G, Rajabi Mashhadi H (2016) Robust and stable gene selection via maximum–minimum correntropy criterion. Genomics 83–87. doi:10.1016/j.ygeno.2015.12.006

  • Neri F, Tirronen V (2010) Recent advances in differential evolution: a survey and experimental analysis. Artif Intell Rev 61–106. doi:10.1007/s10462-009-9137-2

  • Noghabi HS, Mashhadi HR, Shojaei K (2015) Differential evolution with generalized mutation operator. arXiv preprint arXiv:1510.02516

  • Price KV (1999) An introduction to differential evolution. In: David C, Marco D, Fred G, Dipankar D, Pablo M, Riccardo P, Kenneth VP (eds) New ideas in optimization. McGraw-Hill Ltd, UK, pp 79–108

    Google Scholar 

  • Price K, Storn RM, Lampinen JA (2005) Differential evolution: a practical approach to global optimization (natural computing series). Springer, New York

    MATH  Google Scholar 

  • Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evolut Comput 398–417. doi:10.1109/TEVC.2008.927706

  • Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 341–359. doi:10.1023/A:1008202821328

  • Suganthan PN, Hansen N, Liang JJ, Deb Y-PCK, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC2005 special session on real-parameter optimization. Nanyang Technol University, Singapore

    Google Scholar 

  • Thomas B, David BF, Zbigniew M (1997) Handbook of evolutionary computation. IOP Publishing Ltd, Bristol

    MATH  Google Scholar 

  • Wei-Jie Y, Meie S, Wei-Neng C, Zhi-Hui Z, Yue-Jiao G, Ying L, Ou L, Jun Z (2014) Differential evolution with two-level parameter adaptation. IEEE Trans Cybern 1080–1099. doi:10.1109/TCYB.2013.2279211

  • Yong W, Zixing C, Qingfu Z (2011) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evolut Comput 55–66. doi:10.1109/TEVC.2010.2087271

Download references

Acknowledgments

The authors would like to thank Center of Excellence on Soft Computing and Intelligent Information Processing (SCIIP) for kind supports and Dr. Y. Wang for making the MATLAB code of jDE available online.

Author information

Authors and Affiliations

  1. Center of Excellence on Soft Computing and Intelligent Information Processing (SCIIP), Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

    Hossein Sharifi-Noghabi, Habib Rajabi Mashhadi & Kambiz Shojaee

  2. Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

    Hossein Sharifi-Noghabi, Habib Rajabi Mashhadi & Kambiz Shojaee

  3. Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

    Habib Rajabi Mashhadi

Authors
  1. Hossein Sharifi-Noghabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Habib Rajabi Mashhadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kambiz Shojaee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Habib Rajabi Mashhadi.

Ethics declarations

Conflict of interest

Authors have nothing to disclose regarding conflict of interests.

Additional information

Communicated by A. Di Nola.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharifi-Noghabi, H., Rajabi Mashhadi, H. & Shojaee, K. A novel mutation operator based on the union of fitness and design spaces information for Differential Evolution. Soft Comput 21, 6555–6562 (2017). https://doi.org/10.1007/s00500-016-2359-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-016-2359-8

Keywords

Search

Navigation