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

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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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Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham University, Coimbatore, India

    G. Jeyakumar & C. Shunmuga Velayutham

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  1. G. Jeyakumar
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  2. C. Shunmuga Velayutham
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Correspondence to G. Jeyakumar.

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Communicated by Y. Jin.

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Jeyakumar, G., Shunmuga Velayutham, C. Distributed heterogeneous mixing of differential and dynamic differential evolution variants for unconstrained global optimization. Soft Comput 18, 1949–1965 (2014). https://doi.org/10.1007/s00500-013-1178-4

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  • DOI: https://doi.org/10.1007/s00500-013-1178-4

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