Fast Mixed Strategy Differential Evolution Using Effective Mutant Vector Pool

  • Hao Liu
  • Han Huang
  • Yingjun Wu
  • Zhenhua Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7331)


The mutant vector has significant influence on the performance of Differential Evolution (DE). Different mutant vector always generates different result, one outstanding mutant vector for a specify problem perhaps achieve unbearable bad result for another question. There still no one perfect mutant vector can solve all problems excellently. In this situation, mixed strategy method is proposed to improve the performance of DE by combining multi-effective mutant vectors together. This paper proposes a fast mixed strategy DE (FMDE). The new method uses two best mutant vectors selected from the mutant vector pool and applies a fast mixed method to generate better result without increase computing expense. The FMDE is evaluated by 27 benchmarks selected from Congress on Evolutionary Computation (CEC) competition. The experiment result shows FMDE is competitive, stable and comprehensive. abstract environment.


Differential Evolution (DE) mutant vector mixed strategy fast method 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Storn, R., Price, K.V.: Differential Evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optimization 11(4), 341–359 (1997)MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Neri, F., Tirronen, V.: Recent Advances in Differential Evolution: A Survey and Experimental Analysis. Artif. Intell. Rev. 33(1), 61–106 (2010)CrossRefGoogle Scholar
  3. 3.
    Das, S., Suganthan, P.N.: Differential evolution: A survey of the state-of-the-art. IEEE Trans. on Evolutionary Computation (2011), doi:10.1109/TEVC.2010.2059031Google Scholar
  4. 4.
    Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(2), 398–417 (2009)CrossRefGoogle Scholar
  5. 5.
    Wang, Y., Cai, Z., Zhang, Q.: Differential Evolution with Composite Trial Vector Generation Strategies and Control Parameters. IEEE Trans. Evol. Comput. 15(1), 55–66 (2011)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V.: Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Trans. Evolut. Comput. 10(6), 646–657 (2006)CrossRefGoogle Scholar
  7. 7.
    Mezura-Montes, E., Velazquez-Reyes, J., Coello, C.A.: A comparative study of differential evolution variants for global optimization. Proc. Genet. Evol. Comput., 485–492 (2006)Google Scholar
  8. 8.
    Caponio, A., Neri, F.: Differential Evolution with Noise Analyzer. In: Giacobini, M., Brabazon, A., Cagnoni, S., Di Caro, G.A., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Fink, A., Machado, P. (eds.) EvoWorkshops 2009. LNCS, vol. 5484, pp. 715–724. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Liu, B., Zhang, X., Ma, H.: Hybrid differential evolution for noisy optimization. In: Proc. IEEE Congr. Evol. Comput., vol. 2, pp. 1691–1698 (2005, 2009)Google Scholar
  10. 10.
    Zhan, Z., Zhang, J., Li, Y., Chung, H.S.: Adaptive Particle Swarm Optimization. IEEE Trans. On Systems, Man, and Cybernetics 39(6), 1362–1381 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hao Liu
    • 1
  • Han Huang
    • 1
    • 2
  • Yingjun Wu
    • 1
  • Zhenhua Huang
    • 1
  1. 1.School of Software EngineeringSouth China University of TechnologyGuangzhouP.R. China
  2. 2.Department of Management SciencesCollege of Business City University of Hong KongHong Kong

Personalised recommendations