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The Memetic Ant Colony Optimization with Directional Derivatives Simplex Algorithm for Time Delays Identification

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Book cover Computational Collective Intelligence. Technologies and Applications (ICCCI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6922))

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Abstract

The identification of time delay in the linear plant is important tasks. Most of the conventional identification techniques, such as those based on least mean-squares, are essentially gradient-guided local search techniques and they require a smooth search space or a differentiable performance index. New possibility in this field is opened by an application of the hybrid Ant Colony Optimization (ACO) with local optimization algorithm. The Directional Derivatives Simplex (DDS) as a local optimization algorithm is proposed in the paper and used in the memetic ACODDS method. The ACODDS algorithm is compared with ACO and a classical methods: Global Separable Nonlinear Least Squares (GSNLS). The obtained results suggest that the proposed method performs well in estimating the model parameters.

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References

  1. Bjorklund, S., Ljung, L.: A Review of Time-Delay Estimation Techniques. In: Proceedings of the IEEE Conference on Decision and Control 2003, Maui, Hawaii, USA, vol. 3, pp. 2502–2507 (2003)

    Google Scholar 

  2. Boukas, E.K.: Stochastic output feedback of uncertain time-delay system with saturing actuators. Journal of optimization theory and applications 118(2), 255–273 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  3. Li, Z.-S., Wan, Y.-C.: Suboptimal control for plants with pure time delay based on state feedback. Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University 36(suppl.), 138–140 (2002)

    Google Scholar 

  4. Chen, X., Wang, M.: Global optimization methods for time delay estimation. In: Proceedings of the World Congress on Intelligent Control and Automation (WCICA), vol. 1, pp. 212–215 (2004)

    Google Scholar 

  5. Chen, B.-S., Hung, J.-C.: A global estimation for multichannel time-delay and signal parameters via genetic algorithm. Signal Processing 81(5), 1061–1067 (2001)

    Article  MATH  Google Scholar 

  6. Harada, K., Kobayashi, Y., Okita, T.: Identification of Linear Systems With Time Delay and Unknown Order Electrical. Engineering in Japan (English translation of Denki Gakkai Ronbunshi) 145(3), 61–68 (2003)

    Google Scholar 

  7. Previdi, F., Lovera, M.: Identification of non-linear parametrically varying models using separable least squares. Int. J. Control 77(16), 1382–1392 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  8. Olinsky, A.D., Quinn, J.T., Mangiameli, P.M., Chen, S.K.: A genetic algorithm approach to nonlinear least squares estimation. Int. J. Math. Educ. SCI. Tehnol. 35(2), 207–217 (2004)

    Article  MathSciNet  Google Scholar 

  9. Papliński, J.P.: An evolutionary algorithm for identification of non-stationary linear plants with time delay. In: Proceedings of the First International Conference of Informatics in Control, Automation and Robotics (ICINCO), vol. 1, pp. 64–69 (2004)

    Google Scholar 

  10. Phat, V.N., Savkin, A.V.: Robust state estimation for a class of uncertain time-delay systems. Systems and Control Letters 47(3), 237–245 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  11. Shaltaf, S.: Neural-Network-Based Time-Delay Estimation Eurasip. Journal on Applied Signal Processing 3, 378–385 (2004)

    Article  Google Scholar 

  12. Yang, Z., Iemura, H., Kanae, S., Wada, K.: A global nonlinear instrumental variable method for identification of continous-time systems with unknown time delays. IFAC World Congres, Prague, Czech Republic (2005)

    Google Scholar 

  13. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)

    MATH  Google Scholar 

  14. Westwick, D.T., Kearney, R.E.: Separable least squares identification of nonlinear Hammerstein models: application to stretch reflex dynamics. Annals of Biomedical Engineering 29, 707–718 (2001)

    Article  Google Scholar 

  15. Dorigo, M., Stűtzle, T.: Ant colony optimization. MIT Pres, Cambridge (2004)

    MATH  Google Scholar 

  16. Andries, P.: Engelbrecht. Fundamentals of Computational Swarm Intelligence. Wiley and Sons, Ltd., England (2006)

    Google Scholar 

  17. Agosta, W.C.: Chemical communication – the Language of Pheromone. W.H. Freeman and Company, New York (1992)

    Google Scholar 

  18. Socha, K., Dorigo, M.: Ant colony optimization for continuous domains. European Journal of Operational Research 185, 1155–1173 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  19. Spendley, W., Hext, G.R., Himsworth, F.R.: Sequential application of simplex design in optimization and evolutionary operation. Technometrics 4, 441–461 (1962)

    Article  MathSciNet  MATH  Google Scholar 

  20. Nelder, J.A., Mead, R.: A simplex method for function minimization. The Computer Journal 7, 308–313 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  21. Ryan, P.B., Barr, R.L., Tod, H.D.: Simplex Techniques for Nonlinear Optimization. Anal. Chem. 52(9), 1460–1467 (1980)

    Article  Google Scholar 

  22. Umeda, T., Kawa, A.I.: A Modified Complex Method for Optimization. Ind. Eng. Chem. Process Des. Develop. 10(2), 229–236 (1971)

    Article  Google Scholar 

  23. Glover, F., Kochenberger, G.A. (eds.): Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol. 57. Kluwer Academic Publishers/Springer, New York, USA (2003)

    MATH  Google Scholar 

  24. Moscato, P.: Memetic algorithms. In: Handbook of Applied Optimization, ch. 3.6.4, pp. 157–167. Oxford University Press, Oxford (2002)

    Google Scholar 

  25. Dawkins, R.: The Selfish Gene, 1st edn. Oxford University Press, Oxford (1976), 2 edn. (October 1989)

    Google Scholar 

  26. Mavrovouniotis, M., Yang, S.: A memetic ant colony optimization algorithm for the dynamic traveling salesman problem. In: Soft Computing - A Fusion of Foundations, Methodologies and Applications (2010)

    Google Scholar 

  27. Wang, F., Qiu, Y.-h.: Multimodal Function Optimizing by a New Hybrid Nonlinear Simplex Search and Particle Swarm Algorithm. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 759–766. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  28. Bharth, B., Borkar, V.S.: Stochastic approximation algorithms: overview and resent trends. Sādhanā India 24, Parts 4 & 5, 425–452 (1999)

    Google Scholar 

  29. Iemura, H., Yang, Z., Kanae, S., Wada, K.: Identification of continous-time systems with unknow time delays by global nonlinear least-squares method. In: IFAC Workshop on Adaptation and Lerning in Control and Signal Processing, Yokohama, Japan (2004)

    Google Scholar 

  30. Papliński, J.P.: Hybrid genetic and Nelder-Mead algorithms for identification of time delay. In: Proceedings of the 14th IEEE IFAC International Conference Methods and Models in Automation and Robotics (MMAR), Międzyzdroje, Poland (2009)

    Google Scholar 

  31. Papliński, J.P.: The genetic algorithm with simplex crossover for identification of time delay. In: Intelligent information Systems, New Approaches, pp. 337–346. Publishing hous of University of Podlasie (2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Control and Measurement, West Pomeranian University of Technology, 26 Kwietnia 10, 71-126, Szczecin, Poland

    Janusz P. Papliński

Authors
  1. Janusz P. Papliński
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Editors and Affiliations

  1. Gdynia Maritime University, Morska 81-87, 81-225, Gdynia, Poland

    Piotr Jędrzejowicz

  2. Wroclaw University of Technology, Wyb. Wyspianskiego 27, 50-370, Wroclaw, Poland

    Ngoc Thanh Nguyen

  3. University of Information Technology Vietnam, Km 20, Xa lo Ha Noi, Linh Trung, Thu Duc, 848, HCM City, Vietnam

    Kiem Hoang

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Papliński, J.P. (2011). The Memetic Ant Colony Optimization with Directional Derivatives Simplex Algorithm for Time Delays Identification. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2011. Lecture Notes in Computer Science(), vol 6922. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23935-9_18

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  • DOI: https://doi.org/10.1007/978-3-642-23935-9_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23934-2

  • Online ISBN: 978-3-642-23935-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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