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Comparing GPU-parallelized metaheuristics to branch-and-bound for batch plants optimization

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Abstract

We systematically compare two approaches with the optimal design of multiproduct batch plants that are widely used, e.g., in the chemical industry. Deterministic algorithms like branch-and-bound achieve global optimality, but often require a prohibitively high computation effort. We propose an alternative, hybrid algorithm by combining two metaheuristics: ant colony optimization (ACO) and simulated annealing (SA), in order to find near-optimal solutions in a reasonable time. We develop a parallel implementation of our hybrid approach on graphics processing units using CUDA. We experimentally compare both approaches, and we show that our hybrid metaheuristic approach \((\hbox {ACO}+\hbox {SA})\) yields almost-optimal solutions, while computing them significantly faster than when using branch-and-bound.

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Authors and Affiliations

  1. Tambov State Technical University, Tambov, Russia

    Andrey Borisenko

  2. University of Muenster, Münster, Germany

    Sergei Gorlatch

Authors
  1. Andrey Borisenko
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  2. Sergei Gorlatch
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Corresponding author

Correspondence to Andrey Borisenko.

Additional information

This work was supported by DAAD and by the Ministry of Education and Science of the Russian Federation under the “Mikhail Lomonosov II” Program, and by the HPC2SE project of BMBF. We thank Nvidia Corporation for the donated hardware used in our experiments.

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Borisenko, A., Gorlatch, S. Comparing GPU-parallelized metaheuristics to branch-and-bound for batch plants optimization. J Supercomput 75, 7921–7933 (2019). https://doi.org/10.1007/s11227-018-2472-9

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  • DOI: https://doi.org/10.1007/s11227-018-2472-9

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