Soft Computing

, Volume 21, Issue 22, pp 6555–6562 | Cite as

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

  • Hossein Sharifi-Noghabi
  • Habib Rajabi Mashhadi
  • Kambiz Shojaee
Foundations

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.

Keywords

Differential Evolution Union Differential Evolution Intelligent selection Ranking-based mutation Proximity-based mutation 

References

  1. 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
  2. Bäck T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, OxfordMATHGoogle Scholar
  3. 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
  4. Boussaïd I, Lepagnot J, Siarry P (2013) A survey on optimization metaheuristics. Inf Sci 82–117. doi:10.1016/j.ins.2013.02.041
  5. 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 89–110CrossRefGoogle Scholar
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. Gong W, Cai Z (2013) Differential evolution with ranking-based mutation operators. IEEE Trans Cybern 2066–2081. doi:10.1109/TCYB.2013.2239988
  13. 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 615–626Google Scholar
  14. 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
  15. 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
  16. 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
  17. 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 71–78Google Scholar
  18. 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
  19. Michalewicz Z (1996) Genetic algorithms + data structures = evolution programs, 3rd edn. Springer, BerlinCrossRefMATHGoogle Scholar
  20. 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
  21. 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
  22. Noghabi HS, Mashhadi HR, Shojaei K (2015) Differential evolution with generalized mutation operator. arXiv preprint arXiv:1510.02516
  23. 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 79–108Google Scholar
  24. Price K, Storn RM, Lampinen JA (2005) Differential evolution: a practical approach to global optimization (natural computing series). Springer, New YorkMATHGoogle Scholar
  25. 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
  26. 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
  27. 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, SingaporeGoogle Scholar
  28. Thomas B, David BF, Zbigniew M (1997) Handbook of evolutionary computation. IOP Publishing Ltd, BristolMATHGoogle Scholar
  29. 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
  30. 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

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Hossein Sharifi-Noghabi
    • 1
    • 2
  • Habib Rajabi Mashhadi
    • 1
    • 2
    • 3
  • Kambiz Shojaee
    • 1
    • 2
  1. 1.Center of Excellence on Soft Computing and Intelligent Information Processing (SCIIP), Faculty of EngineeringFerdowsi University of MashhadMashhadIran
  2. 2.Department of Computer EngineeringFerdowsi University of MashhadMashhadIran
  3. 3.Department of Electrical EngineeringFerdowsi University of MashhadMashhadIran

Personalised recommendations