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    Two Algorithmic Enhancements for the Parallel Differential Evolution

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    Date
    2011-01
    Author
    Neri, Ferrante;
    Weber, Matthieu;
    Tirronen, Ville
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    Abstract
    This paper proposes the use of two algorithms based on the parallel differential evolution. The first algorithm proposes the use of endemic control parameters within a parallel differential evolution algorithm; the differential evolution running at each subpopulation is associated with randomly initialised scale factor and crossover rate, which are then repeatedly updated during the optimisation process. The second algorithm proposes decomposing the search space of large-scale problems into lower-dimensionality subspaces, and associating each of these to one subpopulation of a parallel differential evolution algorithm. Each subpopulation is running a modified differential evolution algorithm, where the crossover function is limited to components of the subpopulation’s associated subspace. According to numerical results, both algorithms seem to be clear improvements over the original parallel distributed evolution; they are simple, robust, and efficient algorithms suited for various applications.
    Description
    Citation : Weber, M., Neri, F. and Tirronen V (2011) Two Algorithmic Enhancements for the Parallel Differential Evolution. International Journal of Innovative Computing and Applications, 3 (1), pp. 20-30
    URI
    http://hdl.handle.net/2086/6810
    DOI
    http://dx.doi.org/10.1504/IJICA.2011.037948
    ISSN : 1751-648X
    Research Group : Centre for Computational Intelligence
    Research Institute : Institute of Artificial Intelligence (IAI)
    Peer Reviewed : Yes
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    • School of Computer Science and Informatics [2970]

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