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01-10-2014 | Methodologies and Application

Distributed heterogeneous mixing of differential and dynamic differential evolution variants for unconstrained global optimization

Authors: G. Jeyakumar, C. Shunmuga Velayutham

Published in: Soft Computing | Issue 10/2014

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Abstract

This paper proposes a novel distributed differential evolution framework called distributed mixed variants (dynamic) differential evolution (\(dmvD^{2}E)\). This novel framework is a heterogeneous mix of effective differential evolution (DE) and dynamic differential evolution (DDE) variants with diverse characteristics in a distributed framework to result in \(dmvD^{2}E\). The \(dmvD^{2}E\), discussed in this paper, constitute various proportions and combinations of DE/best/2/bin and DDE/best/2/bin as subpopulations with each variant evolving independently but also exchanging information amongst others to co-operatively enhance the efficacy of \(dmvD^{2}E\) as whole. The \(dmvD^{2}E\) variants have been run on 14 test problems of 30 dimensions to display their competitive performance over the distributed classical and dynamic versions of the constituent variants. The \(dmvD^{2}E\), when benchmarked on a different 13 test problems of 500 as well as 1,000 dimensions, scaled well and outperformed, on an average, five existing distributed differential evolution algorithms.

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Title: Distributed heterogeneous mixing of differential and dynamic differential evolution variants for unconstrained global optimization
Authors: G. Jeyakumar
C. Shunmuga Velayutham
Publication date: 01-10-2014
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 10/2014
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-013-1178-4

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