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Inverse Continuous Casting Problem Solved by Applying the Artificial Bee Colony Algorithm

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Artificial Intelligence and Soft Computing (ICAISC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7895))

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Abstract

The paper presents an application of the Artificial Bee Colony algorithm in solving the inverse continuous casting problem consisted in reconstruction of selected parameters characterizing the cooling conditions in crystallizer and in secondary cooling zone. In presented approach we propose to use the bee algorithm for minimization of appropriate functional representing the crucial part of the method.

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References

  1. Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs (1995)

    MATH  Google Scholar 

  2. Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Computing 8, 687–697 (2007)

    Article  Google Scholar 

  3. Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214, 108–132 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  4. Eberhart, R.C., Shi, Y., Kennedy, J.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  5. Beni, G., Wang, J.: Swarm intelligence in cellular robotic systems. In: Proceed. NATO Advanced Workshop on Robots and Biological Syst., Tuscany (1989)

    Google Scholar 

  6. Chu, S.-C., Tsai, P.-w., Pan, J.-S.: Cat swarm optimization. In: Yang, Q., Webb, G. (eds.) PRICAI 2006. LNCS (LNAI), vol. 4099, pp. 854–858. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  7. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control System Magazine 22, 52–67 (2002)

    Article  Google Scholar 

  8. Mehrabian, R., Lucas, C.: A novel numerical optimization algorithm inspired from weed colonization. Ecological Informatics 1(4), 355–366 (2006)

    Article  Google Scholar 

  9. Geem, Z.W.: Improved Harmony Search from ensemble of music players. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds.) KES 2006. LNCS (LNAI), vol. 4251, pp. 86–93. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  10. Lučic̀, P., Teodorovic̀, D.: Computing with Bees: attacking complex transportation engineering problems. Int. J. Artificial Intelligence Tools 12, 375–394 (2003)

    Article  Google Scholar 

  11. Tereshko, V.: Reaction-diffusion model of a honeybee colony’s foraging behaviour. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 807–816. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  12. Özbakir, L., Baykasoǧlu, A., Tapkan, P.: Bees algorithm for generalized assignment problem. Appl. Math. Comput. 215, 3782–3795 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  13. Hetmaniok, E., Słota, D., Zielonka, A.: Solution of the inverse heat conduction problem by using the ABC algorithm. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds.) RSCTC 2010. LNCS, vol. 6086, pp. 659–668. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  14. Zielonka, A., Hetmaniok, E., Słota, D.: Using of the Artificial Bee Colony Algorithm for determining the heat transfer coefficient. In: Czachórski, T., Kozielski, S., Stańczyk, U. (eds.) Man-Machine Interactions 2. Advances in Intelligent Systems and Computing, vol. 103, pp. 369–376. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  15. Hetmaniok, E., Słota, D., Zielonka, A., Wituła, R.: Comparison of ABC and ACO algorithms applied for solving the inverse heat conduction problem. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) EC 2012 and SIDE 2012. LNCS, vol. 7269, pp. 249–257. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  16. Mochnacki, B., Suchy, J.: Numerical Methods in Computations of Foundry Processes. PFTA, Cracow (1995)

    Google Scholar 

  17. Grever, W., Binder, A., Engl, H., Mörhwald, K.: Optimal cooling strategies in continuous casting of steel with variable casting speed. Inverse Probl. Eng. 2, 289–300 (1997)

    Google Scholar 

  18. Constalas, D., Kačur, J., Van Keer, R.: On optimal cooling strategy for variable-speed continuous casting. Int. J. Numer. Methods Engrg. 53, 539–565 (2002)

    Article  Google Scholar 

  19. Nowak, I., Nowak, A.J., Wrobel, L.C.: Identification of phase change fronts by Bezier splines and BEM. Int. J. Therm. Sci. 41, 492–499 (2002)

    Article  Google Scholar 

  20. Santos, C.A., Spim, J.A., Garcia, A.: Mathematical modeling and optimization strategies (genetic algorithm and knowledge base) applied to the continuous casting of steel. Eng. Appl. Artif. Intel. 16, 511–527 (2003)

    Article  Google Scholar 

  21. Santos, C.A., Garcia, A., Frick, C.R., Spim, J.A.: Evaluation of heat transfer coefficient along the secondary cooling zoones in the continuous casting of steel billets. Inverse Probl. Sci. Eng. 14, 687–700 (2006)

    Article  MATH  Google Scholar 

  22. Słota, D.: Identification of the cooling condition in 2-D and 3-D continuous casting processes. Numer. Heat Transfer B 55, 155–176 (2009)

    Article  Google Scholar 

  23. Słota, D.: Restoring boundary conditions in the solidification of pure metals. Comput. & Structures 89, 48–54 (2011)

    Article  Google Scholar 

  24. Beck, J.V., Blackwell, B., St.Clair, C.R.: Inverse Heat Conduction: Ill Posed Problems. Wiley Intersc., New York (1985)

    MATH  Google Scholar 

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Hetmaniok, E., Słota, D., Zielonka, A., Pleszczyński, M. (2013). Inverse Continuous Casting Problem Solved by Applying the Artificial Bee Colony Algorithm. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7895. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38610-7_40

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  • DOI: https://doi.org/10.1007/978-3-642-38610-7_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38609-1

  • Online ISBN: 978-3-642-38610-7

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