Intermediate Population Based Differential Evolution Algorithm

  • Tarun Kumar Sharma
  • Millie Pant
Part of the Communications in Computer and Information Science book series (CCIS, volume 250)


In the present paper propose two novel variants of Differential Evolution (DE), named IP-OBL and IP-NSDE, have been proposed. In IP-OBL the initial population is generated by using the intermediate positions between the uniformly generated random numbers and opposition based numbers. While in case of IP-NSDE, the initial population is generated as an intermediate of uniform random numbers and numbers generated by Nelder Mead Method. The proposed algorithms are further modified by selecting best NP/2 individuals to perform in population evolution. The modified variants are termed as MIP-OBL and MIP-NSDE. The numerical results of 10 benchmark problems indicate the competence of the proposed algorithms.


Differential Evolution Opposition Based Learning Nelder Mead 


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  1. 1.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  2. 2.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Harbor (1975)Google Scholar
  3. 3.
    Brest, J., Greiner, S., et al.: Self-adapting Control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation 10(6), 646–657 (2006)CrossRefGoogle Scholar
  4. 4.
    Das, S., Konar, A., Chakraborty, U.: Two improved differential evolution schemes for faster global search. In: ACMSIGEVO Proceedings of GECCO, Washington, D.C, pp. 991–998 (2005)Google Scholar
  5. 5.
    Storn, R., Price, K.: Differential Evolution – a simple and efficient Heuristic for global optimization over continuous spaces. Journal Global Optimization 11, 341–359 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Tizhoosh, H.R.: Opposition-based learning: A new scheme for machine intelligence. In: Proc. Int. Conf. Comput. Intell. Modeling Control and Autom., Vienna, Austria, vol. I, pp. 695–701 (2005)Google Scholar
  7. 7.
    Nelder, J.A., Mead, R.: A simplex method for, function minimisation. The Computer Journal 7, 308–313 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Jia, L., Gong, W., Wu, H.: An Improved Self-Adaptive Control Parameter of Differential Evolution For Global Optimization. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds.) ISICA 2009. CCIS, vol. 51, pp. 215–224. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Tarun Kumar Sharma
    • 1
  • Millie Pant
    • 1
  1. 1.Indian Institute of TechnologyRoorkeeIndia

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