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Scale factor inheritance mechanism in distributed differential evolution

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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.

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Acknowledgments

This research is supported by the Academy of Finland, Akatemiatutkija 00853, Algorithmic Design Issues in Memetic Computing.

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

    Matthieu Weber, Ville Tirronen & Ferrante Neri

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  1. Matthieu Weber
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  2. Ville Tirronen
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  3. Ferrante Neri
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Correspondence to Ferrante Neri.

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Weber, M., Tirronen, V. & Neri, F. Scale factor inheritance mechanism in distributed differential evolution. Soft Comput 14, 1187–1207 (2010). https://doi.org/10.1007/s00500-009-0510-5

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