Shuffle Or Update Parallel Differential Evolution for Large-Scale Optimization

dc.contributor.authorNeri, Ferranteen
dc.contributor.authorWeber, Matthieuen
dc.contributor.authorTirronen, Villeen
dc.date.accessioned2012-08-13T10:02:49Z
dc.date.available2012-08-13T10:02:49Z
dc.date.issued2011-10
dc.description.abstractThis paper proposes a novel algorithm for large-scale optimization problems. The proposed algorithm, namely shuffle or update parallel differential evolution (SOUPDE) is a structured population algorithm characterized by sub-populations employing a Differential evolution logic. The sub-populations quickly exploit some areas of the decision space, thus drastically and quickly reducing the fitness value in the highly multi-variate fitness landscape. New search logics are introduced into the subpopulation functioning in order to avoid a diversity loss and thus premature convergence. Two simple mechanisms have been integrated in order to pursue this aim. The first, namely shuffling, consists of randomly rearranging the individuals over the sub-populations. The second consists of updating all the scale factors of the sub-populations. The proposed algorithm has been run on a set of various test problems for five levels of dimensionality and then compared with three popular meta-heuristics. Rigorous statistical and scalability analyses are reported in this article. Numerical results show that the proposed approach significantly outperforms the meta-heuristics considered in the benchmark and has a good performance despite the high dimensionality of the problems. The proposed algorithm balances well between exploitation and exploration and succeeds to have a good performance over the various dimensionality values and test problems present in the benchmark. It succeeds at outperforming the reference algorithms considered in this study. In addition, the scalability analysis proves that with respect to a standard Differential Evolution, the proposed SOUPDE algorithm enhances its performance while the dimensionality grows.en
dc.identifier.citationWeber, M., Neri, F. and Tirronen, V. (2011) Shuffle Or Update Parallel Differential Evolution for Large-Scale Optimization, Soft Computing - A Fusion of Foundations, Methodologies and Applications, 15 (11), pp. 2089-2107en
dc.identifier.doihttps://doi.org/10.1007/s00500-010-0640-9
dc.identifier.issn1432-7643
dc.identifier.urihttp://hdl.handle.net/2086/6809
dc.language.isoenen
dc.peerreviewedYesen
dc.publisherSpringeren
dc.researchgroupCentre for Computational Intelligenceen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectdifferential evolutionen
dc.subjectdistributed algorithmsen
dc.subjectLarge-Scale optimizationen
dc.subjectrandomizationen
dc.subjectscale factor updateen
dc.subjectshuffling mechanismen
dc.titleShuffle Or Update Parallel Differential Evolution for Large-Scale Optimizationen
dc.typeArticleen

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