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Modified Self-adaptive Strategy for Controlling Parameters in Differential Evolution

  • Tam Bui
  • Hieu Pham
  • Hiroshi Hasegawa
Part of the Communications in Computer and Information Science book series (CCIS, volume 324)

Abstract

In this paper, we propose a new technical to modify the self-adaptive Strategy for Controlling Parameters in Differential Evolution algorithm (MSADE). The DE algorithm has been used in many practical cases and has demonstrated good convergence properties. It has only a few control parameters as NP (Number of Particles), F (scaling factor) and CR (crossover), which are kept fixed throughout the entire evolutionary process. However, these control parameters are very sensitive to the setting of the control parameters based on their experiments. The value of control parameters depend on the characteristics of each objective function, so we have to tune their value in each problem that mean it will take too long time to perform. We present a new version of the DE algorithm for obtaining self-adaptive control parameter settings that show good performance on numerical benchmark problems.

Keywords

Differential Evolution (DE) Global search Multi-peak problems Local search 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tam Bui
    • 1
  • Hieu Pham
    • 1
  • Hiroshi Hasegawa
    • 2
  1. 1.Graduate School of Engineering and ScienceShibaura Institute of TechnologyJapan
  2. 2.College of Systems Engineering and ScienceShibaura Institute of TechnologyJapan

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