The Optimal Allocation of Finishing Train in Steel Rolling Based on Improved Genetic Algorithm

Article Preview

Abstract:

The central issue of finishing train is that we should distribute the thickness of each exit with reason and determine the rolling force and relative convexity. The optimization methods currently used are empirical distribution method and the load curve method, but they both have drawbacks. To solve those problems we established a mathematical model of the finishing train and introduced an improved Genetic Algorithm. In this algorithm we used real number encoding, selection operator of a roulette and elitist selection and then improved crossover and mutation operators. The results show that the model and algorithm is feasible and could ensure the optimal effect and convergence speed. The products meet the production requirements.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

720-724

Citation:

Online since:

October 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Z. L. Cui. Design and Implementation about the optimization of the rolling schedules based on improved Genetic Algorithms [D], Shandong: Shandong University, 2007. (in Chinese).

Google Scholar

[2] K. Lua, J. Croweroft, M. Pias, et al., Communication Surveys & Tutorials, IEEE, 2005, 7(2): 72-93.

Google Scholar

[3] J. H. Holland, Evolutionary Computation, 2000, 8(4): 373-391.

Google Scholar

[4] C. Lv, Q. L. Zhao, L. Z. Liu etc., Iron and Steel Research, 2001, 13(1): 26一29. (in Chinese).

Google Scholar

[5] F. Xhafa, X. Herrero, et., Journal of computer and system sciences, 2013, 79: 1086–1100.

Google Scholar

[6] Y. H. Li, S. S. Ning etc., Control Engineering, 2004, 11(4): 321-324. (in Chinese).

Google Scholar

[7] J. Suzuki, IEEE Trans on Systems, Man and Cybernetics, 1998, 25(4): 655-699.

Google Scholar

[8] Y. Zhu, C. Dovrolis, M. Ammar, Computer Networks: The International Journal of Computer and Telecommunications Networking, 2006, 50(6): 742-762.

Google Scholar

[9] E. Afzalan, M. Joorabian., International Journal of Electrical Power & Energy Systems, 2013, 52: 55-67.

Google Scholar