Parallelizing Metaheuristics for Optimal Design of Multiproduct Batch Plants on GPU

  • Andrey BorisenkoEmail author
  • Sergei Gorlatch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10421)


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.


Hybrid metaheuristics Ant Colony Optimization Simulated Annealing GPU computing CUDA Parallel metaheuristics Combinatorial optimization Multiproduct batch plant design 



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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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Tambov State Technical UniversityTambovRussia
  2. 2.University of MuensterMuensterGermany

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