Hidden Markov Models Training by a Particle Swarm Optimization Algorithm

  • Sébastien Aupetit
  • Nicolas Monmarché
  • Mohamed Slimane


In this work we consider the problem of Hidden Markov Models (HMM) training. This problem can be considered as a global optimization problem and we focus our study on the Particle Swarm Optimization (PSO) algorithm. To take advantage of the search strategy adopted by PSO, we need to modify the HMM's search space. Moreover, we introduce a local search technique from the field of HMMs and that is known as the Baum–Welch algorithm. A parameter study is then presented to evaluate the importance of several parameters of PSO on artificial data and natural data extracted from images.

Mathematics Subject Classifications (2000):

68T05 [68Q32, 91E40] 68C35 [82C80] 90C59 

Key words

particle swarm optimization hidden markov models training image learning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Abido, M.: Optimal power flow using particle swarm optimization, Electr. Energy Syst. 24 (2002), 563–571.CrossRefGoogle Scholar
  2. 2.
    Baum, L., Petrie, T., Soules, G. and Weiss, N.: A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains, Ann. Math. Stat. 41(1) (1970), 164–171.MathSciNetMATHGoogle Scholar
  3. 3.
    Berg, F. v.: An analysis of Particle Swarm Optimizers, PhD thesis, University of Pretoria, 2001.Google Scholar
  4. 4.
    Bilmes, J.: What HMMs can do? Technical report, Department of Electrical Engineering, University of Washington, 2002.Google Scholar
  5. 5.
    Brouard, T., Slimane, M. and Asselin de Beauville, J.-P.: Modélisation de processus simultanés indépendants par chaînes de Markov multidimensionnelles (CMC-MD/I), Technical Report 200, Laboratoire d'Informatique de l'Université de Tours, E3i Tours, 1997.Google Scholar
  6. 6.
    Cappé, O.: Ten years of HMM. http://ww.tsi.enst.fr/~cappe/docs/hmmbib.html, 2001.
  7. 7.
    Chen, T.-Y., Mei, X.-D., Pan, J.-S. and Sun, S.-H.: Optimization of HMM by the Tabu Search Algorithm, J. Inf. Sci. Eng. 20(5) (2004), 949–957.Google Scholar
  8. 8.
    Dempster, A. P., Laird, N. M. and Rubin, D. B.: Maximum-likelihood from incomplete data via the EM algorithm, J. R. Stat. Soc. B 39(1) (1977), 1–39.MATHMathSciNetGoogle Scholar
  9. 9.
    Do, M.: Fast approximation of Kullback–Leibler distance for dependence trees and Hidden Markov Models, IEEE Signal Process. Lett. 10(8) (2003), 250–254.MathSciNetGoogle Scholar
  10. 10.
    Falkhausen, M., Reininger, H. and Wolf, D.: Calculation of distance measures between Hidden Markov Models, in Proceedings of the Eurospeech'95. Madrid, 1995, pp. 1487–1490.Google Scholar
  11. 11.
    Fine, S., Singer, Y. and Tishby, N.: The Hierarchical Hidden Markov Model: Analysis and applications, Mach. Learn. 32(1) (1998), 41–62.MATHCrossRefGoogle Scholar
  12. 12.
    Forney, G.: The Viterbi algorithm, Proc. IEEE 61(3) (1973), 268–278.MathSciNetCrossRefGoogle Scholar
  13. 13.
    Hu, X., Eberhard, R. and Shi, Y.: Swarm intelligence for Permutation Optimization: A case-study of n-Queens problems, in Proceedings of the IEEE Swarm Intelligence Symposium. Indianapolis, 2003, pp. 243–246.Google Scholar
  14. 14.
    Jelinek, F., Bahl, L. and Mercer, L.: Design of a linguistic statistical decoder for the recognition of continuous speech, in: IEEE Trans. IT, IT-21, 1975.Google Scholar
  15. 15.
    Kennedy, J. and Eberhart, R.: Particle Swarm Optimization, in Proceedings of the IEEE International Joint Conference on Neural Networks, Vol. 4, 1995, pp. 1942–1948, IEEE.Google Scholar
  16. 16.
    Kennedy, J., Eberhart, R. and Shi, Y.: Swarm Intelligence, Morgan Kaufmann, 2002.Google Scholar
  17. 17.
    Kosmala, A., Willett, D. and Rigoll, G.: Advanced state clustering for very large vocabulary HMM-based on-line handwriting recognition, in Proceeding of the International Conference on Document Analysis and Recognition, 1999, pp. 442–445.Google Scholar
  18. 18.
    Kundu, A. and Bahl, P.: Recognition of handwritten script: A hidden Markov model based approach, in ICASSP, 1988, pp. 928–931.Google Scholar
  19. 19.
    Monmarché, N.: Algorithmes de fourmis artificielles : Applicationsàla classification et à l'optimisation, Thèse de doctorat, Laboratoire d'Informatique, Université de Tours, 2000.Google Scholar
  20. 20.
    Monmarché, N., Venturini, G. and Slimane, M.: Optimisation de Chaînes Markov Cachées par une population de fourmis Pachycondyla apicalis, Rapport interne 195, Laboratoire d'Informatique de l'Université de Tours, E3i Tours, 1997, 24 pages.Google Scholar
  21. 21.
    Monmarché, N., Venturini, G. and Slimane, M.: On how Pachycondyla apicalis ants suggest a new search algorithm, Future Gener. Comput. Syst. 16(8) (2000), 937–946.CrossRefGoogle Scholar
  22. 22.
    Olivier, C., Avila, M., Courtellemont, P., Paquet, T. and Lecourtier, Y.: Handwritten word recognition by image segmentation and Hidden Markov Model, in Proceedings of IEEE-IES, 1993, pp. 2093–2097.Google Scholar
  23. 23.
    Omran, M., Salman, A. and Engelbrecht, A.: Image classification using Particle Swarm Optimization, in 4th Asia-Pacific Conference on Simulated Evolution and Learning, 2002.Google Scholar
  24. 24.
    Paquet, U. and Engelbrecht, A.: Training support vector machines with particle swarms, in International Joint Conference on Neural Networks, Portland, Oregon, 2003.Google Scholar
  25. 25.
    Rabiner, L.: A tutorial on Hidden Markov Models and selected applications in speech recognition, Proc. IEEE 77(2) (1989), 257–286.CrossRefGoogle Scholar
  26. 26.
    Rasmussen, T. K. and Krink, T.: Improved Hidden Markov Model training for multiple sequence alignment by a particle swarm optimization–evolutionary algorithm hybrid, Biosystems 72(1–2) (2003), 5–17.CrossRefGoogle Scholar
  27. 27.
    Ratnaweera, A., Halgamuge, S. and Watson, H.: Self-organizing hierarchical Particle Swarm Optimizer with time-varying acceleration coefficients, IEEE Trans. Evol. Comput. 8(3) (2004), 240–255.CrossRefGoogle Scholar
  28. 28.
    Salman, A., Ahmad, I. and Al-Madani, S.: Particle Swarm Optimization for task assignment problem, Microprocess. Microsyst. 26 (2003), 363–371.CrossRefGoogle Scholar
  29. 29.
    Samaria, F. and Harter, A.: Paramatrisation of a stochastic model for human face identification, in 2nd IEEE Workshop on application of computer vision, Sarasota, Florida, 1994.Google Scholar
  30. 30.
    Slimane, M., Venturini, G., Asselin de Beauville, J.-P. and Brouard, T.: Hybrid genetic learning of hidden Markov models for Time Series Prediction, Biomimetic Approaches in Management Science, Kluwer, 1998.Google Scholar
  31. 31.
    Slimane, M., Venturini, G., Asselin de Beauville, J.-P., Brouard, T. and Brandeau, A.: Optimizing Hidden Markov Models with a Genetic Algorithm, in Proceedings of Artificial Evolution conference, Vol. 1063 of Lecture Notes in Computer Science, Springer, 1996, pp. 384–396.Google Scholar
  32. 32.
    Tasgetiren, M., Sevkli, M., Liang, Y. and Gencyilmaz, G.: Particule Swarm Optimization Algorithm for Single Machine Total Weighted Tardiness Problem, in Proceedings of Congress on Evolutionary Computation, 2004, pp. 1412–1419.Google Scholar
  33. 33.
    Tasgetiren, M.F., Sevkli, M., Liang, Y.-C. and Gencyilmaz, G.: Particle Swarm Optimization Algorithm for Permutation Flowshop, in M. Dorigo, M. Birattari, C. Blum, L. Gambardella, F. Mondada, and T. Stützle (eds.): Proceedings of ANTS 2004 – Fourth International Workshop on Ant Colony Optimization and Swarm Intelligence, Vol. 3172 of Lecture Notes in Computer Science, Brussels, Belgium, Springer, 2004.Google Scholar
  34. 34.
    Thomsen, R.: Evolving the topology of hidden Markov models using evolutionary algorithms, in Proceedings of Parallel Problem Solving from Nature VII (PPSN-2002), 2002, pp. 861–870.Google Scholar
  35. 35.
    van der Merwe, D. and Engelbrecht, A.: Data clustering using Particle Swarm Optimization, in IEEE Congress on Evolutionary Computation, Canberra, Australia, 2003, pp. 215–220.Google Scholar
  36. 36.
    Vihola, M., Harju, M., Salmela, P., Suontausta, J. and Savela, J.: Two dissimilarity measures for HMMs and their application in phoneme model clustering, in Proceedings of ICASSP 2002, 1980, pp. 993–936.Google Scholar
  37. 37.
    Wachowiak, M., Smolikova, R., Zheng, Y., Zurada, J. and Elmaghraby, A.: An approach to multimodal biomedical image registration utilizing Particle Swarm Optimization, IEEE Trans. Evol. Comput. 8(3) (2004), 289–301.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  • Sébastien Aupetit
    • 1
  • Nicolas Monmarché
    • 1
  • Mohamed Slimane
    • 1
  1. 1.Laboratoire d'Informatique, Polytech'ToursUniversité François-Rabelais de ToursToursFrance

Personalised recommendations