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Optimizing a GPU-Parallelized Ant Colony Metaheuristic by Parameter Tuning

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Parallel Computing Technologies (PaCT 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11657))

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Abstract

We address the problem of accelerating the GPU-parallelized Ant Colony Optimization (ACO) metaheuristic used for an important class of optimization problems – design of multiproduct batch plants, with a particular use case of a Chemical-Engineering System (CES). We propose and implement a novel approach to ACO’s parameter tuning, with the following advantages compared to previous work: we accelerate tuning by using GPU, and we do not require additional constructs like function mapping in fuzzy logic, algorithms for online-tuning, etc. We report our experimental results that confirm the efficiency of parameter tuning and the advantages of our approach.

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Acknowledgements

We are grateful to the anonymous reviewers for their very helpful comments, and to the Nvidia Corp. for the donated hardware used in our experiments. This work was supported by the DAAD (German Academic Exchange Service) and by the Ministry of Education and Science of the Russian Federation under the “Mikhail Lomonosov II”-Programme, and by the HPC2SE project of BMBF (Federal Ministry of Education and Research, Germany).

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Authors and Affiliations

  1. Tambov State Technical University, Tambov, Russia

    Andrey Borisenko

  2. University of Muenster, Münster, Germany

    Sergei Gorlatch

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  1. Andrey Borisenko
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  2. Sergei Gorlatch
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Correspondence to Andrey Borisenko .

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Editors and Affiliations

  1. Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk State University, Novosibirsk State Technical University, Novosibirsk, Russia

    Victor Malyshkin

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Borisenko, A., Gorlatch, S. (2019). Optimizing a GPU-Parallelized Ant Colony Metaheuristic by Parameter Tuning. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2019. Lecture Notes in Computer Science(), vol 11657. Springer, Cham. https://doi.org/10.1007/978-3-030-25636-4_12

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  • DOI: https://doi.org/10.1007/978-3-030-25636-4_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-25635-7

  • Online ISBN: 978-3-030-25636-4

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