Abstract
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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References
Aarts, E., Korst, J., Michiels, W.: Simulated annealing. In: Search Methodologies, pp. 265–285. Springer Science & Business Media, Heidelberg (2014)
Agarwal, K., Sinha, A., Hima Bindu, M.: A novel hybrid approach to N-Queen problem. In: Wyld, D., Zizka, J., Nagamalai, D. (eds.) Advances in Computer Science, Engineering & Applications. AISC, vol. 166, pp. 519–527. Springer, Heidelberg (2012). doi:10.1007/978-3-642-30157-5_52
Aguilar-Lasserre, A.A., Bautista, M.A.B., Ponsich, A., Huerta, M.A.G.: An AHP-based decision-making tool for the solution of multiproduct batch plant design problem under imprecise demand. Comput. Oper. Res. 36(3), 711–736 (2009)
Birattari, M.: Tuning Metaheuristics: A Machine Learning Perspective. Springer, Heidelberg (2009)
Borisenko, A.B., Karpushkin, S.V.: Hierarchy of processing equipment configuration design problems for multiproduct chemical plants. J. Comput. Syst. Sci. Int. 53(3), 410–419 (2014)
Borisenko, A., Haidl, M., Gorlatch, S.: A GPU parallelization of branch-and-bound for multiproduct batch plants optimization. J. Supercomput. 73(2), 639–651 (2017)
Borisenko, A., Kegel, P., Gorlatch, S.: Optimal design of multi-product batch plants using a parallel branch-and-bound method. In: Malyshkin, V. (ed.) PaCT 2011. LNCS, vol. 6873, pp. 417–430. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23178-0_36
Dawson, L., Stewart, I.: Improving ant colony optimization performance on the GPU using CUDA. In: 2013 IEEE Congress on Evolutionary Computation, pp. 1901–1908. IEEE, June 2013
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)
Dietz, A., Azzaro-Pantel, C., Pibouleau, L., Domenech, S.: Strategies for multiobjective genetic algorithm development: Application to optimal batch plant design in process systems engineering. Comput. Ind. Eng. 54(3), 539–569 (2008)
Dorigo, M., Blum, C.: Ant colony optimization theory: a survey. Theoret. Comput. Sci. 344(2–3), 243–278 (2005)
Dorigo, M., Stützle, T.: Ant colony optimization: overview and recent advances. In: Gendreau, M., Potvin, J.-Y. (eds.) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol. 146, pp. 227–263. Springer, New York (2010). doi:10.1007/978-1-4419-1665-5_8
El Hamzaoui, Y., Bassam, A., Abatal, M., Rodríguez, J.A., Duarte-Villaseñor, M.A., Escobedo, L., Puga, S.A.: Flexibility in biopharmaceutical manufacturing using particle swarm algorithms and genetic algorithms. In: Schütze, O., Trujillo, L., Legrand, P., Maldonado, Y. (eds.) NEO 2015. SCI, vol. 663, pp. 149–171. Springer, Cham (2017). doi:10.1007/978-3-319-44003-3_7
Gandomi, A.H., Yang, X.S., Talatahari, S., Alavi, A.H.: Metaheuristic algorithms in modeling and optimization. In: Metaheuristic Applications in Structures and Infrastructures, pp. 1–24. Elsevier BV (2013)
Gonzalez-Pardo, A., Camacho, D.: A new CSP graph-based representation for ant colony optimization. In: 2013 IEEE Congress on Evolutionary Computation, pp. 689–696. Institute of Electrical and Electronics Engineers (IEEE), June 2013
Kallioras, N.A., Kepaptsoglou, K., Lagaros, N.D.: Transit stop inspection and maintenance scheduling: a GPU accelerated metaheuristics approach. Transp. Res. Part C Emerg. Technol. 55, 246–260 (2015)
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. Institute of Electrical and Electronics Engineers (IEEE), December 2009
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P., et al.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Lee, T.S., Moslemipour, G., Ting, T.O., Rilling, D.: A novel hybrid ACO/SA approach to solve stochastic dynamic facility layout problem (SDFLP). In: Huang, D.-S., Gupta, P., Zhang, X., Premaratne, P. (eds.) ICIC 2012. CCIS, vol. 304, pp. 100–108. Springer, Heidelberg (2012). doi:10.1007/978-3-642-31837-5_15
NVIDIA Corporation: CUDA C programming guide 8.0, September 2016. http://docs.nvidia.com/cuda/pdf/CUDA_C_Programming_Guide.pdf
Ponsich, A., Coello, C.C.: Differential evolution performances for the solution of mixed-integer constrained process engineering problems. Appl. Soft Comput. 11(1), 399–409 (2011)
Pourvaziri, H., Azimi, P.: A tuned-parameter hybrid algorithm for dynamic facility layout problem with budget constraint using GA and SAA. J. Optim. Ind. Eng. 7(15), 65–75 (2014)
Rossi, F., Van Beek, P., Walsh, T.: Handbook of Constraint Programming. Elsevier, Amsterdam (2006)
Solnon, C.: Ant Colony Optimization and Constraint Programming. Wiley Inc., Hoboken (2010)
Stützle, T., López-Ibánez, M., Pellegrini, P., Maur, M., de Oca, M.M., Birattari, M., Dorigo, M.: Parameter adaptation in ant colony optimization. In: Hamadi, Y., Monfroy, E., Saubion, F. (eds.) Autonomous Search, pp. 191–215. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21434-9_8
Valadi, J., Siarry, P.: Applications of Metaheuristics in Process Engineering. Springer Science & Business Media, Heidelberg (2014)
Wei, K.C., Wu, C.C., Yu, H.L.: Mapping the simulated annealing algorithm onto CUDA GPUs. In: 2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), pp. 1–8, November 2015
Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, Bristol (2010)
Acknowledgement
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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Borisenko, A., Gorlatch, S. (2017). Parallelizing Metaheuristics for Optimal Design of Multiproduct Batch Plants on GPU. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2017. Lecture Notes in Computer Science(), vol 10421. Springer, Cham. https://doi.org/10.1007/978-3-319-62932-2_39
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DOI: https://doi.org/10.1007/978-3-319-62932-2_39
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