Abstract
To enhance plate quality of cold rolling strip steel, a method based on Ant Colony Optimization with Quantum Action (ACO-QA) is developed. In this method, each ant position is represented by a group of quantum bits, and a new quantum rotation gates are designed to update the position of the ant. In order to makes full efficiency, a pretreatment using fuzzy method is firstly adapted before resolving the mathematical model with ACO-QA. This method overcomes the shortcoming of ACO, which is easy to fall into local optimums and has a slow convergence rate in continuous space. At last, a field cognition system is designed to test the efficiency of this method. The results show that it can validly identify almost all defection patterns, compared to traditional identification system. The recognition precision of this method is higher and can meet the shape recognition requirements of cold rolling strip steel.
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Zhang, J., Wang, Y. (2012). Defection Recognition of Cold Rolling Strip Steel Based on ACO Algorithm with Quantum Action. In: Pan, Z., Cheok, A.D., Müller, W., Chang, M., Zhang, M. (eds) Transactions on Edutainment VII. Lecture Notes in Computer Science, vol 7145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29050-3_26
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DOI: https://doi.org/10.1007/978-3-642-29050-3_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29049-7
Online ISBN: 978-3-642-29050-3
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