Two Algorithmic Enhancements for the Parallel Differential Evolution

De Montfort University Open Research Archive

Show simple item record Neri, Ferrante en Weber, Matthieu en Tirronen, Ville en 2012-08-13T10:07:07Z 2012-08-13T10:07:07Z 2011-01
dc.identifier.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 en
dc.identifier.issn 1751-648X
dc.description.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. en
dc.language.iso en en
dc.publisher Inderscience en
dc.subject evolutionary algorithm en
dc.subject differential evolution en
dc.subject DE en
dc.subject structured population en
dc.subject parallel differential evolution en
dc.subject PDE en
dc.subject component decomposition en
dc.subject endemic control parameters en
dc.title Two Algorithmic Enhancements for the Parallel Differential Evolution en
dc.type Article en
dc.researchgroup Centre for Computational Intelligence en
dc.peerreviewed Yes en

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