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2017 | OriginalPaper | Buchkapitel

Parallelizing Metaheuristics for Optimal Design of Multiproduct Batch Plants on GPU

verfasst von : Andrey Borisenko, Sergei Gorlatch

Erschienen in: Parallel Computing Technologies

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Parallel Algorithm for Solving Constrained Global Optimization Problems

Nächstes Kapitel The Optimization of Traffic Management for Cloud Application and Services in the Virtual Data Center

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Titel: Parallelizing Metaheuristics for Optimal Design of Multiproduct Batch Plants on GPU
verfasst von: Andrey Borisenko
Sergei Gorlatch
Verlag: Springer International Publishing
Buch: Parallel Computing Technologies
Print ISBN: 978-3-319-62931-5

Electronic ISBN: 978-3-319-62932-2

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-62932-2_39

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