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Optimizing a GPU-Parallelized Ant Colony Metaheuristic by Parameter Tuning

  • Andrey BorisenkoEmail author
  • Sergei Gorlatch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11657)

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.

Keywords

Constraint Satisfaction Problem Ant Colony Optimization Tuning metaheuristics Parallel metaheuristics GPU computing Multi-product batch plant design 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

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

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