Scale factor inheritance mechanism in distributed differential evolution

  • Authors:

  • Matthieu Weber;Ville Tirronen;Ferrante Neri

  • Affiliations:

  • University of Jyväskylä, Department of Mathematical Information Technology, P.O. Box 35, 40014, Agora, Finland;University of Jyväskylä, Department of Mathematical Information Technology, P.O. Box 35, 40014, Agora, Finland;University of Jyväskylä, Department of Mathematical Information Technology, P.O. Box 35, 40014, Agora, Finland

  • Venue:
  • Soft Computing - A Fusion of Foundations, Methodologies and Applications
  • Year:
  • 2010

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Abstract

This article proposes a distributed differential evolution which employs a novel self-adaptive scheme, namely scale factor inheritance. In the proposed algorithm, the population is distributed over several sub-populations allocated according to a ring topology. Each sub-population is characterized by its own scale factor value. With a probabilistic criterion, that individual displaying the best performance is migrated to the neighbor population and replaces a pseudo-randomly selected individual of the target sub-population. The target sub-population inherits not only this individual but also the scale factor if it seems promising at the current stage of evolution. In addition, a perturbation mechanism enhances the exploration feature of the algorithm. The proposed algorithm has been run on a set of various test problems and then compared to two sequential differential evolution algorithms and three distributed differential evolution algorithms recently proposed in literature and representing state-of-the-art in the field. Numerical results show that the proposed approach seems very efficient for most of the analyzed problems, and outperforms all other algorithms considered in this study.