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Jaya optimization algorithm with GPU acceleration

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Abstract

Optimization methods allow looking for an optimal value given a specific function within a constrained or unconstrained domain. These methods are useful for a wide range of scientific and engineering applications. Recently, a new optimization method called Jaya has generated growing interest because of its simplicity and efficiency. In this paper, we present the Jaya GPU-based parallel algorithms we developed and analyze both parallel performance and optimization performance using a well-known benchmark of unconstrained functions. Results indicate that parallel Jaya implementation achieves significant speed-up for all benchmark functions, obtaining speed-ups of up to \(190\times \), without affecting optimization performance.

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Authors and Affiliations

  1. Department of Computer Technology, University of Alicante, 03071, Alicante, Spain

    A. Jimeno-Morenilla, J. L. Sánchez-Romero & H. Mora-Mora

  2. Department of Physics and Computer Architecture, Miguel Hernández University, 03202, Elche, Spain

    H. Migallón

Authors
  1. A. Jimeno-Morenilla
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  2. J. L. Sánchez-Romero
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  3. H. Migallón
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  4. H. Mora-Mora
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Corresponding author

Correspondence to H. Migallón.

Additional information

This research was supported by the Spanish Ministry of Economy and Competitiveness under Grant TIN2015-66972-C5-4-R, co-financed by FEDER funds(MINECO/FEDER/UE).

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Jimeno-Morenilla, A., Sánchez-Romero, J.L., Migallón, H. et al. Jaya optimization algorithm with GPU acceleration. J Supercomput 75, 1094–1106 (2019). https://doi.org/10.1007/s11227-018-2316-7

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  • DOI: https://doi.org/10.1007/s11227-018-2316-7

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