Asynchronous Differential Evolution

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


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.


Global optimization derivative-free optimization genetic algorithm evolution strategy 


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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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