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Differential Evolution Algorithm: Recent Advances

  • Ponnuthurai Nagaratnam Suganthan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7505)

Abstract

Differential Evolution (DE) has been a competitive stochastic realparameter optimization algorithm since it was introduced in 1995. DE possesses computational steps similar to a standard Evolutionary Algorithm (EA). DE perturbs the population members with the scaled differences of distinct population members. Hence, a step-size parameter used in algorithms such as evolutionary programming and evolution strategy is not required to be specified. Due to its consistent robust performance, DE has drawn the attention of many researchers all over the world. This article presents a brief review of the recent DE-variants for bound constrained single objective, multi-objective and multimodal optimization problems. It also suggests potential applications of DE in remanufacturing.

Keywords

Differential evolution differential mutation linearly scalable exponential crossover ensemble differential evolution multimodal multiobjective evolutionary algorithms 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ponnuthurai Nagaratnam Suganthan
    • 1
  1. 1.School of Electrical and Electronic EngineeringNanyang Technological UniversitySingapore

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