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Parallelizing Metaheuristics for Optimal Design of Multiproduct Batch Plants on GPU

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10421))

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Abstract

We propose a metaheuristics-based approach to the optimal design of multi-product batch plants, with a particular application example of chemical-engineering systems. Our hybrid approach combines two metaheuristics: Ant Colony Optimization (ACO) and Simulated Annealing (SA). We develop a sequential implementation of the proposed method and we parallelize it on Graphics Processing Units (GPU) using the CUDA programming environment. We experimentally demonstrate that the results of our hybrid metaheuristic approach (ACO+SA) are very near to the global optimal solutions, but they are produced much faster than using the deterministic Branch-and-Bound approach.

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Acknowledgement

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, as well as by the German Research Agency (DFG) in the framework of the Cluster of Excellence CiM at the University of Muenster. We also thank the Nvidia Corp. for the donated hardware used in our experiments.

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Authors and Affiliations

  1. Tambov State Technical University, Tambov, Russia

    Andrey Borisenko

  2. University of Muenster, Muenster, Germany

    Sergei Gorlatch

Authors
  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. Russian Academy of Sciences, Novosibirsk, Russia

    Victor Malyshkin

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Borisenko, A., Gorlatch, S. (2017). Parallelizing Metaheuristics for Optimal Design of Multiproduct Batch Plants on GPU. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2017. Lecture Notes in Computer Science(), vol 10421. Springer, Cham. https://doi.org/10.1007/978-3-319-62932-2_39

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  • DOI: https://doi.org/10.1007/978-3-319-62932-2_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62931-5

  • Online ISBN: 978-3-319-62932-2

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