Abstract
The reasonable rolling schedule is not only beneficial to improve the accuracy and achieve good shape of cold rolled steel strip, but also has practical value in prolonging the service life of equipment and improving the production efficiency of enterprise. It tends to reach premature convergence when particle swarm optimization algorithm is applied in the optimization of rolling schedule. Based on the rapid convergence of particle swarm optimization algorithm and evenly traversal of Tent sequence, a collaborative optimization algorithm which combines particle swarm optimization algorithm with chaos searching is introduced in this paper. The proposed algorithm can overcome the disadvantage that particle swarm optimization algorithm easily falls into the local minimum, and find pressure rate satisfying the preset target function using less iteration times and optimal time, and realize the optimal rate target.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pires, C.T.A., Ferreira, H.C., Sales, R.M., et al.: Set-up Optimization for Tandem Cold Mills: a Case Study. Journal of Materials Processing Technology 173(3), 368–375 (2006)
Zhang, Y., Liu, X., Wang, G.: Research on Plate Rolling Load Distribution Based on Data Mining. Iron and Steel 40(4), 44–45 (2005)
Li, H., Xu, J., Gong, D., et al.: Application of Momentum Technique in Load Distribution for Tandem Hot Strip Mill. Iron and Steel 41(2), 46–50 (2006)
Li, Y., Liu, J., Wang, Y.: An improved adaptive weight approach GA for optimizing multi-objective rolling schedules in a tandem cold rolling. Control Theory and Applications 26(6), 687–693 (2009)
Wei, L., Li, X., Li, Y., et al.: Optimization of Tandem Cold Rolling Schedule Based on Improved Adaptive Genetic Algorithm. Journal of Mechanical Engineering 46(16), 136–141 (2010)
Yang, J., Dou, F., Liu, S., et al.: Application of genetic algorithm to rolling schedule in tandem cold mill. China Mechanical Engineering 18(15), 1868–1871 (2007)
Li, Y., Liu, J.C., Wang, Y.: An Adaptive Weight PSO for Rolling Schedules Multi-objective Optimization of Tandem Cold Rolling. In: Proceedings of the IEEE International Conference on Automation and Logistics, vol. 8, pp. 895–899 (2009)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. of IEEE Int. Conf. on Piscataway, pp. 1942–1948 (1995)
Kennedy, J.: The Particle Swarm: Social Adaptation of Knowledge. In: Proceedings of the IEEE International Conference on Evolutionary Computation, vol. 4, pp. 303–308 (1997)
Hong, T., Peng, G., Li, Z.P., Liang, Y.: A Novel Evolutionary Strategy for Particle Swarm Optimization. Chinese Journal of Electronics 18(4), 771–774 (2009)
Shan, L., Qiang, H.: Chaotic optimization algorithm based on Tent map. Control and Decision 2, 179–182 (2005)
Zhang, X., Wen, S., Li, H.: Chaotic Particle Swarm Optimization Algorithm Based on Tent Mapping. China Mechanical Engineering 19(17), 2108–2112 (2008)
Meng, H., Zheng, P.: Particle Swarm Optimization Algorithm Based on Chaotic Series. Control and Decision 3, 263–266 (2006)
Eberhart, R.C., Shi, Y.: Particle swarm optimization. In: Proc. of Congress on Evolutionary Computation, pp. 81–88 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ying, L., Jing-sheng, W., Hong-rui, W., Li-xin, W. (2012). Optimization of Tandem Cold Rolling Schedule Based on Collaborative Optimized PSO. In: Liu, C., Wang, L., Yang, A. (eds) Information Computing and Applications. ICICA 2012. Communications in Computer and Information Science, vol 307. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34038-3_55
Download citation
DOI: https://doi.org/10.1007/978-3-642-34038-3_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34037-6
Online ISBN: 978-3-642-34038-3
eBook Packages: Computer ScienceComputer Science (R0)