Distance-Based Analysis for Base Vector Selection in Mutation Operation of Differential Evolution Algorithm

  • A. R. Khaparde
  • M. M. Raghuwanshi
  • L. G. Malik
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 335)


There is a remarkable performance of differential evolution (DE) algorithm on continuous space problem. Mutation plays a very vital role in success of DE but in traditional DE all the vectors are selected in random manner. Sometimes, it gives a random exploration in search space. Here, the distance-based analysis for mutation vector selection is carried out and distance-based criteria for base vector (reference point) selection have proposed. Experimentation is conducted on eight standard uni-model and multi-model functions. Later, the results have compared with standard DE and other variant of DE. Experiments show that the proposed strategy has a very steady and stable exploration of search space.


Differential evolution algorithm Analysis 


  1. 1.
    Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)CrossRefMATHMathSciNetGoogle Scholar
  2. 2.
    Storn, R., Price, K.: Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. TR-95-012, March 1995Google Scholar
  3. 3.
    Karabo, D., Okdem, S.: A simple and global optimization algorithm for engineering problems: differential evolution algorithm. Turk. J. Electr. Eng. 12(1) (2004)Google Scholar
  4. 4.
    Lampinen, O., Zelinka, I.: “On Stagnation of the Differential Evolution Algorithm”. In: Ošmera, P. (ed.) Proceedings of MENDEL 2000, 6th International Mendel Conference on Soft Computing, Brno, Czech Republic, 7–9 June 2000 Google Scholar
  5. 5.
    Gamperle, R., Muller, S.: A parameter study for differential evolution. In: Proceeding WSEAS international conference on advances in intelligent Systems,fuzzysystems, evolutionary computation, pp. 293–298, 2002Google Scholar
  6. 6.
    Zaharie, D.: Differential evolution: From theoretical analysis to practical insightsGoogle Scholar
  7. 7.
    Montes, E., Coello, C.C.: A comparative study of differential evolution variants for global optimization, Seattle, Washington, USA, GECCO’06, 8–12 July 2006Google Scholar
  8. 8.
    Zaharie, D.: A comparative analysis of crossover variants in differential evolution. In: Proceedings of pp. 171–181, ISSN 1896-7094 c 2007 PIPSGoogle Scholar
  9. 9.
    Zaharie, D.: Influence of Crossover on Behavior of Differential Evolution. Elsevier, Amsterdam (2009)Google Scholar
  10. 10.
    Ao, Y., Chi, H.: Experimental Study on Differential Evolution Strategies Global Congress on Intelligent Systems, 2009, IEEEGoogle Scholar
  11. 11.
    Wenyin, G., Zhihua, C.: An empirical study on differential evolution for optimal power allocation in WSNs. In: 8th International Conference on Natural Computation (2012)Google Scholar
  12. 12.
    Chattopadhyay, S., Sanyal, S., Chandra, A.: Comparison of various mutation schemes of differential evolution algorithm for the design of low pass FIR filter, (SEISCON 2011)Google Scholar
  13. 13.
    Zhou, R., Hao, J., Cao, H., Fan, H.: An Empirical Study on Differential Evolution Algorithm and its Several Variants (ICEMEAI 2011)Google Scholar
  14. 14.
    Epitropakis, M.G., Plagianakos, V.P., Vrahatis, M.N.: Balancing the Exploration and Exploitation Capabilities of the Differential Evolution Algorithm, 2008, IEEEGoogle Scholar
  15. 15.
    Lou, Y., Li, J., Shi, Y.: A Differential Evolution Based on Individual-Sorting and Individual-Sampling Strategies, 2011, IEEEGoogle Scholar
  16. 16.
    Price, K.V., Rönkkönen, J.: Comparing the Uni-Modal Scaling Performance of Global and Local Selection in a Mutation-Only Differential Evolution Algorithm CEC, Canada 16–21 July 2006Google Scholar
  17. 17.
    Bhowmik, P.I., Das, S., Konar, A., Das, S., Nagar, A.K.: A new differential evolution with improved mutation strategy. IEEE Congr. Evol. Comput. 1–8, (2010)Google Scholar
  18. 18.
    Epitropakis, M.G., Tasoulis, D.K., Pavlidis, N.G.: Enhancing differential evolution utilizing proximity-based mutation operators. IEEE TEC 15(1), 99–119 (2011)Google Scholar
  19. 19.
    Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Tran. Evol. Comput. 15(1), 4–31 (2011)CrossRefGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • A. R. Khaparde
    • 1
  • M. M. Raghuwanshi
    • 2
  • L. G. Malik
    • 3
  1. 1.Information TechnologyG.H. Raisoni College of Engineering, Rajiv Gandhi College of Engineering and ResearchNagpurIndia
  2. 2.Computer TechnologyYeshwantrao Chavan College of EngineeringNagpurIndia
  3. 3.G. H.Raisoni College of EngineeringNagpurIndia

Personalised recommendations