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International Workshop on Machine Learning, Optimization, and Big Data

MOD 2016: Machine Learning, Optimization, and Big Data pp 317–329Cite as

Cultural Ant Colony Optimization on GPUs for Travelling Salesman Problem

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10122))

Abstract

Ant Colony Optimization (ACO) is a well-established metaheuristic successfully applied to solve hard combinatorial optimization problems, including Travelling Salesman Problem (TSP). However, ACO algorithm as many population-based approaches has some disadvantages, such as lower solution quality and longer computational time. To overcome these issues, parallel Cultural Ant Colony Optimization (pCACO) is introduced in this paper. The proposed approach hybridises Cultural Algorithm with ACO-based \(\mathcal {MAX}\)-\(\mathcal {MIN}\) Ant System. pCACO has been implemented on Graphics Processing Units (GPUs) using CUDA programming model. Through testing nine benchmark asymmetric TSP problems, the experimental results show the new method enhances the solution quality when compared to sequential and parallel ACO, yielding comparable computational time to parallel ACO.

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References

  1. Alba, E., Leguizamon, G., Ordonez, G.: Two models of parallel ACO algorithms for the minimum tardy task problem. Int. J. High Perform. Syst. Archit. 1(1), 50–59 (2007)

    Article  Google Scholar 

  2. Bullnheimer, B., Hartl, R.F., Strauß, C.: A new rank based version of the ant system: a computational study. Central Eur. J. Oper. Res. Econ. 7(1), 25–38 (1999)

    MathSciNet  MATH  Google Scholar 

  3. Bullnheimer, B., Kotsis, G., Strauß, C.: Parallelization strategies for the ant system. In: De Leone, R., Murli, A., Pardalos, P.M., Toraldo, G. (eds.) High Performance Algorithms and Software in Nonlinear Optimization. Applied Optimization, vol. 24, pp. 87–100. Springer, Boston (1998). doi:10.1007/978-1-4613-3279-4_6

    Chapter  Google Scholar 

  4. Catala, A., Jaen, J., Mocholi, J.A.: Strategies for accelerating ant colony optimization algorithms on graphical processing units. In: IEEE Congress on Evolutionary Computation, CEC 2007, pp. 492–500. IEEE (2007)

    Google Scholar 

  5. Chu, D., Zomaya, A.: Parallel ant colony optimization for 3D protein structure prediction using the HP lattice model. In: Nedjah, N., de Macedo, M.L., Alba, E. (eds.) Parallel Evolutionary Computations. Studies in Computational Intelligence, vol. 22, pp. 177–198. Springer, Heidelberg (2006). doi:10.1007/3-540-32839-4_9

    Chapter  Google Scholar 

  6. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  7. 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)

    Article  Google Scholar 

  8. Delisle, P., Gravel, M., Krajecki, M., Gagné, C., Price, W.L.: A shared memory parallel implementation of ant colony optimization. In: Proceedings of the 6th Metaheuristics International Conference, pp. 257–264 (2005)

    Google Scholar 

  9. Doerner, K.F., Hartl, R.F., Benkner, S., Lucka, M.: Parallel cooperative savings based ant colony optimization - multiple search and decomposition approaches. Parallel Process. Lett. 16(03), 351–369 (2006)

    Article  MathSciNet  Google Scholar 

  10. Dorigo, M.: Optimization, learning and natural algorithms. Ph.D. thesis, Politecnico di Milano, Italy (1992)

    Google Scholar 

  11. Dorigo, M., Gambardella, L.M.: Ant colonies for the travelling salesman problem. BioSystems 43(2), 73–81 (1997)

    Article  Google Scholar 

  12. Dorigo, M., Maniezzo, V., Colorni, A., Maniezzo, V.: Positive feedback as a search strategy. Technical report 91–016, Dipartimento di Elettronica, Politecnico di Milano, Italy (1991)

    Google Scholar 

  13. Gambardella, L.M., Dorigo, M.: Solving symmetric and asymmetric TSPs by ant colonies. In: International Conference on Evolutionary Computation, pp. 622–627 (1996)

    Google Scholar 

  14. Gambardella, L.M., Dorigo, M., Prieditis, A., Russell, S.: Ant-Q: a reinforcement learning approach to the traveling salesman problem. In: Proceedings of ML 1995, Twelfth International Conference on Machine Learning, pp. 252–260. Morgan Kaufmann (1995)

    Google Scholar 

  15. Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3(2), 95–99 (1988)

    Article  Google Scholar 

  16. Hoffman, K.L., Padberg, M., Rinaldi, G.: Traveling salesman problem. In: Gass, S.I., Fu, M.C. (eds.) Encyclopedia of Operations Research and Management Science, pp. 1573–1578. Springer, New York (2013)

    Chapter  Google Scholar 

  17. Islam, M.T., Thulasiraman, P., Thulasiram, R.K.: A parallel ant colony optimization algorithm for all-pair routing in MANETs. In: Parallel and Distributed Processing Symposium, p. 8. IEEE (2003)

    Google Scholar 

  18. Jiening, W., Jiankang, D., Chunfeng, Z.: Implementation of ant colony algorithm based on GPU. In: 2009 Sixth International Conference on Computer Graphics, Imaging and Visualization, pp. 50–53. IEEE (2009)

    Google Scholar 

  19. Larrañaga, P., Kuijpers, C.M.H., Murga, R.H., Inza, I., Dizdarevic, S.: Genetic algorithms for the travelling salesman problem: a review of representations and operators. Artif. Intell. Rev. 13(2), 129–170 (1999)

    Article  Google Scholar 

  20. Lenstra, J.K., Kan, A.R., Lawler, E.L., Shmoys, D.: The Traveling Salesman Problem. A Guided Tour of Combinatorial Optimization. Wiley, New York (1985)

    MATH  Google Scholar 

  21. Manfrin, M., Birattari, M., Stützle, T., Dorigo, M.: Parallel ant colony optimization for the traveling salesman problem. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds.) ANTS 2006. LNCS, vol. 4150, pp. 224–234. Springer, Heidelberg (2006). doi:10.1007/11839088_20

    Chapter  Google Scholar 

  22. Middendorf, M., Reischle, F., Schmeck, H.: Multi colony ant algorithms. J. Heuristics 8(3), 305–320 (2002)

    Article  MATH  Google Scholar 

  23. Nvidia: Nvidia CUDA (2016). http://nvidia.com/cuda

  24. Reinhelt, G.: TSPLIB: a library of sample instances for the TSP (and related problems) from various sources and of various types (2014). http://comopt.ifi.uniheidelberg.de/software/TSPLIB

  25. Reynolds, R.G.: An introduction to cultural algorithms. In: Proceedings of the Third Annual Conference on Evolutionary Programming, pp. 131–139, Singapore (1994)

    Google Scholar 

  26. Scheuermann, B., So, K., Guntsch, M., Middendorf, M., Diessel, O., ElGindy, H., Schmeck, H.: Fpga implementation of population-based ant colony optimization. Appl. Soft Comput. 4(3), 303–322 (2004)

    Article  Google Scholar 

  27. Stützle, T.: Parallelization strategies for ant colony optimization. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 722–731. Springer, Heidelberg (1998). doi:10.1007/BFb0056914

    Chapter  Google Scholar 

  28. Stützle, T., Dorigo, M.: ACO algorithms for the traveling salesman problem. In: Evolutionary Algorithms in Engineering and Computer Science, pp. 163–183 (1999)

    Google Scholar 

  29. Stützle, T., Hoos, H.: MAX-MIN ant system and local search for the traveling salesman problem. In: 1997 IEEE International Conference on Evolutionary Computation, pp. 309–314. IEEE (1997)

    Google Scholar 

  30. Sun, X., Zhang, Y., Ren, X., Chen, K.: Optimization deployment of wireless sensor networks based on culture-ant colony algorithm. Appl. Math. Comput. 250, 58–70 (2015)

    MathSciNet  MATH  Google Scholar 

  31. Wang, P., Li, H., Zhang, B.: A GPU-based parallel ant colony algorithm for scientific workflow scheduling. Int. J. Grid Distrib. Comput. 8(4), 37–46 (2015)

    Article  Google Scholar 

  32. Wei, K.C., Wu, C.c., Wu, C.J.: Using CUDA GPU to accelerate the ant colony optimization algorithm. In: 2013 International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), pp. 90–95. IEEE (2013)

    Google Scholar 

  33. Wei, X., Han, L., Hong, L.: A modified ant colony algorithm for traveling salesman problem. Int. J. Comput. Commun. Control 9(5), 633–643 (2014)

    Article  Google Scholar 

  34. Xu, J., Zhang, M., Cai, Y.: Cultural ant algorithm for continuous optimization problems. Appl. Math. Inf. Sci 7(2L), 705–710 (2013)

    Article  MathSciNet  Google Scholar 

  35. You, Y.S.: Parallel ant system for traveling salesman problem on GPUs. In: Eleventh Annual Conference on Genetic and Evolutionary Computation, pp. 1–2 (2009)

    Google Scholar 

  36. Yuan, S., Skinner, B., Huang, S., Liu, D.: A new crossover approach for solving the multiple travelling salesmen problem using genetic algorithms. Eur. J. Oper. Res. 228(1), 72–82 (2013)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgments

The work was supported by statutory grant of the Wroclaw University of Science and Technology, Poland.

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Electronics, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 25, 50-370, Wroclaw, Poland

    Olgierd Unold & Radosław Tarnawski

Authors
  1. Olgierd Unold
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  2. Radosław Tarnawski
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Correspondence to Olgierd Unold .

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Editors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  2. Semantic Technology Laboratory, National Research Council (CNR), Catania, Italy

    Piero Conca

  3. Dipartimento di Sociologia e Metodi della Ricerca Sociale, Università di Catania, Catania, Italy

    Giovanni Giuffrida

  4. Department of Mathematics and Computer Science, University of Catania, Catania, Italy

    Giuseppe Nicosia

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Unold, O., Tarnawski, R. (2016). Cultural Ant Colony Optimization on GPUs for Travelling Salesman Problem. In: Pardalos, P., Conca, P., Giuffrida, G., Nicosia, G. (eds) Machine Learning, Optimization, and Big Data. MOD 2016. Lecture Notes in Computer Science(), vol 10122. Springer, Cham. https://doi.org/10.1007/978-3-319-51469-7_27

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  • DOI: https://doi.org/10.1007/978-3-319-51469-7_27

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-51469-7

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