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Asynchronous Differential Evolution

  • Evgeniya Zhabitskaya
  • Mikhail Zhabitsky
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7125)

Abstract

Differential Evolution (DE) is an algorithm to solve possibly nonlinear and non-differentiable global optimization problems. Classical Differential Evolution (CDE) employs a synchronous generation-based evolution strategy. We propose a modification of the CDE algorithm by incorporating mutation, crossover and selection operations into an asynchronous strategy. A novel Asynchronous Differential Evolution (ADE) is well suited for parallel optimization. Moreover even in the sequential mode its rate of convergence is competitive to CDE. The performance of the Asynchronous Differential Evolution is reported on a set of benchmark functions.

Keywords

Global optimization derivative-free optimization genetic algorithm evolution strategy 

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References

  1. 1.
    Das, S., Suganthan, P.N.: Differential Evolution: A Survey of the State-of-the-Art. IEEE Trans. Evol. Comput. 15, 4–31 (2011)CrossRefGoogle Scholar
  2. 2.
    Milani, A., Santucci, V.: Asynchronous Differential Evolution. In: Proc. 2010 IEEE Congr. Evol. Comput., pp. 1210–1216 (2010)Google Scholar
  3. 3.
    Nelder, J.A., Mead, R.: A simplex method for function minimization. Computer J. 7, 308–313 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Price, K., Storn, R.: Differential Evolution — A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. J. of Global Optimization 11, 341–359 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Springer, Heidelberg (2005)zbMATHGoogle Scholar
  6. 6.
    Price, W.L.: A controlled random search procedure for global optimization. Computer J. 20, 367–370 (1977)CrossRefzbMATHGoogle Scholar
  7. 7.
    Qing, A.: Dynamic differential evolution strategy and applications in electromagnetic inverse scattering problems. IEEE Trans. Geosci. Remote Sensing 44, 116–125 (2006)CrossRefGoogle Scholar
  8. 8.
    Suganthan, P.N., et al.: Problem definitions and evaluation criteria for the CEC05 special session on real-parameter optimization. Technical report, Nanyang Technological University, Singapore (2005), http://www.ntu.edu.sg/home/epnsugan/index_files/CEC-05/CEC05.htm

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Evgeniya Zhabitskaya
    • 1
  • Mikhail Zhabitsky
    • 2
    • 3
  1. 1.University DubnaDubnaRussia
  2. 2.Joint Institute for Nuclear ResearchDubnaRussia
  3. 3.Rock Flow DynamicsMoscowRussia

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