Shuffle Or Update Parallel Differential Evolution for Large-Scale Optimization

De Montfort University Open Research Archive

Show simple item record Neri, Ferrante en Weber, Matthieu en Tirronen, Ville en 2012-08-13T10:02:49Z 2012-08-13T10:02:49Z 2011-10
dc.identifier.citation Weber, 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. 2089-2107 en
dc.identifier.issn 1432-7643
dc.description.abstract This 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.language.iso en en
dc.publisher Springer en
dc.subject differential evolution en
dc.subject distributed algorithms en
dc.subject Large-Scale optimization en
dc.subject randomization en
dc.subject scale factor update en
dc.subject shuffling mechanism en
dc.title Shuffle Or Update Parallel Differential Evolution for Large-Scale Optimization en
dc.type Article en
dc.researchgroup Centre for Computational Intelligence en
dc.peerreviewed Yes en

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