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)


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.


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



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).


  1. 1.
    Barbosa, E., Senne, E.: Improving the fine-tuning of metaheuristics: an approach combining design of experiments and racing algorithms. J. Optim. 2017, 1–7 (2017). Scholar
  2. 2.
    Birattari, M.: Tuning Metaheuristics. Studies in Computational Intelligence, vol. 197. Springer, Heidelberg (2009). Scholar
  3. 3.
    Borisenko, A., Gorlatch, S.: Parallelizing metaheuristics for optimal design of multiproduct batch plants on GPU. In: Malyshkin, V. (ed.) PaCT 2017. LNCS, vol. 10421, pp. 405–417. Springer, Cham (2017). Scholar
  4. 4.
    Borisenko, A., Gorlatch, S.: Comparing GPU-parallelized metaheuristics to branch-and-bound for batch plants optimization. J. Supercomput. 1–13 (2018).
  5. 5.
    Borisenko, A., Haidl, M., Gorlatch, S.: A GPU parallelization ofbranch-and-bound for multiproduct batch plants optimization. J. Supercomput. 73(2), 639–651 (2017). Scholar
  6. 6.
    Burtscher, M., Nasre, R., Pingali, K.: A quantitative study of irregular programs on GPUs. In: 2012 IEEE International Symposium on Workload Characterization (IISWC), pp. 141–151. IEEE, November 2012.
  7. 7.
    Castillo, O., Neyoy, H., Soria, J., Melin, P., Valdez, F.: A new approach for dynamic fuzzy logic parameter tuning in ant colony optimization and its application in fuzzy control of a mobile robot. Appl. Soft Comput. 28, 150–159 (2015). Scholar
  8. 8.
    Chen, C.C., Liu, Y.T.: Enhanced ant colony optimization with dynamic mutation and ad hoc initialization for improving the design of TSK-type fuzzy system. Comput. Intell. Neurosci. 2018, 1–15 (2018). Scholar
  9. 9.
    Delévacq, A., Delisle, P., Gravel, M., Krajecki, M.: Parallel ant colony optimization on graphics processing units. J. Parallel Distrib. Comput. 73(1), 52–61 (2013). Scholar
  10. 10.
    Dorigo, M., Birattari, M.: Ant colony optimization. In: Encyclopedia of Machine Learning, pp. 36–39. Springer, Heidelberg (2011). Scholar
  11. 11.
    Dorigo, M., Stützle, T.: Ant colony optimization: overview and recent advances. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics, vol. 272, pp. 311–351. Springer, Cham (2018). Scholar
  12. 12.
    Fallahi, M., Amiri, S., Yaghini, M.: A parameter tuning methodology for metaheuristics based on design of experiments. Int. J. Eng. Technol. Sci. 2(6), 497–521 (2014)Google Scholar
  13. 13.
    Gómez-Cabrero, D., Ranasinghe, D.N.: Fine-tuning the ant colony system algorithm through particle swarm optimization. arXiv preprint arXiv:1803.08353 (2018)
  14. 14.
    Han, T.D., Abdelrahman, T.S.: Reducing branch divergence in GPU programs. In: Proceedings of the Fourth Workshop on General Purpose Processing on Graphics Processing Units - GPGPU-4, pp. 1–3. ACM Press, New York, March 2011.
  15. 15.
    Khan, S., Bilal, M., Sharif, M., Sajid, M., Baig, R.: Solution of n-Queen problem using ACO. In: 2009 IEEE 13th International Multitopic Conference, pp. 1–5. IEEE, December 2009.
  16. 16.
    Li, P., Zhu, H.: Parameter selection for ant colony algorithm based on bacterial foraging algorithm. Math. Probl. Eng. 1–12 (2016). Scholar
  17. 17.
    Mahi, M., Baykan, Ö.K., Kodaz, H.: A new hybrid method based on particle swarm optimization, ant colony optimization and 3-opt algorithms for traveling salesman problem. Appl. Soft Comput. 30, 484–490 (2015). Scholar
  18. 18.
    Maier, H.R., et al.: Ant colony optimization for design of water distribution systems. J. Water Resour. Plann. Manag. 129(3), 200–209 (2003)CrossRefGoogle Scholar
  19. 19.
    NVIDIA Corporation: CUDA C programming guide 10.0, October 2018.
  20. 20.
    NVIDIA Corporation: The NVIDIA CUDA random number generation library (cuRAND), December 2018.
  21. 21.
    Olivas, F., Valdez, F., Castillo, O.: Dynamic parameter adaptation in ant colony optimization using a fuzzy system for TSP problems. In: IFSA-EUSFLAT, pp. 765–770 (2015)Google Scholar
  22. 22.
    Simpson, A., Maier, H., Foong, W., Phang, K., Seah, H., Tan, C.: Selection of parameters for ant colony optimization applied to the optimal design of water distribution systems. In: Proceedings of the International Congress on Modeling and Simulation, Canberra, Australia, pp. 1931–1936 (2001)Google Scholar
  23. 23.
    Skakov, E.S., Malysh, V.N.: Parameter meta-optimization of metaheuristics of solving specific NP-hard facility location problem. J. Phys.: Conf. Ser. 973, 012063 (2018). Scholar
  24. 24.
    Stützle, T., et al.: Parameter adaptation in ant colony optimization. In: Hamadi, Y., Monfroy, E., Saubion, F. (eds.) Autonomous Search, pp. 191–215. Springer, Heidelberg (2011). Scholar
  25. 25.
    Trindade, Á.R., Campelo, F.: Tuning metaheuristics by sequential optimization of regression models. arXiv preprint arXiv:1809.03646, pp. 1–22, September 2018
  26. 26.
    Tsang, E.: Foundations of Constraint Satisfaction: The Classic Text. BoD-Books on Demand, Norderstedt (2014)Google Scholar
  27. 27.
    Valadi, J., Siarry, P.: Applications of Metaheuristics in Process Engineering. Springer, Cham (2014). Scholar
  28. 28.
    Veluscek, M., Kalganova, T., Broomhead, P.: Improving ant colony optimization performance through prediction of best termination condition. In: 2015 IEEE International Conference on Industrial Technology (ICIT), pp. 2394–2402. IEEE, March 2015.
  29. 29.
    Zhang, Z., Feng, Z., Ren, Z.: Approximate termination condition analysis for ant colony optimization algorithm. In: 2010 8th World Congress on Intelligent Control and Automation, pp. 3211–3215. IEEE, July 2010.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

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

Personalised recommendations