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Multi-objective Optimization for Ladle Tracking of Aluminium Tapping Based on NSGA-II

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Bio-inspired Computing: Theories and Applications (BIC-TA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 791))

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Abstract

In order to realize the optimization of ladle tracking of aluminium tapping, a mathematical model, which takes the grade of aluminium, the energy required for transportation and the optimum ratio of aluminium liquid into account, is established. The traditional method optimizes the impurity content and the transport distance based on a single objective optimization, but it requires the empirical values of the weight coefficients. The paper proposes a modified multi-objective optimization model with the elitist non-dominated sorting genetic algorithm (NSGA-II). In the crossover operator process and the mutation operator process, the separately improved methods are introduced based on the ladle tracking problem of aluminium tapping, which replaces the Simulated Binary Crossover (SBX) in the original NSGA-II algorithm into Partially Matched Crossover (PMX) based on natural number coding and uses exchange mutation (EM) operator. Finally, the practical production data of the aluminium factory is used to verify the validity and practicability of the method, and the results show that this method can obtain a feasible solution for the user to choose suitable solutions, and avoid the defects of selecting empirical weighting coefficient.

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Acknowledgments

The work is supported by the National High Technology Research and Development Program of China (863 Program) (No. 2013AA040705 and No. 2013AA041002) and the Fundamental Research Funds for the central Universities (WUT: 2014-IV-142).

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Authors and Affiliations

  1. School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, China

    Kaibo Zhou, Yutao Zou, Hongting Wang & Gaofeng Xu

  2. MOE Key Laboratory of Image Processing and Intelligence Control, Wuhan, 430074, China

    Kaibo Zhou, Yutao Zou, Hongting Wang & Gaofeng Xu

  3. School of Automation, Wuhan University of Technology, Wuhan, 430070, China

    Sihai Guo

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  1. Kaibo Zhou
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  2. Yutao Zou
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  3. Hongting Wang
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  4. Gaofeng Xu
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Corresponding author

Correspondence to Sihai Guo .

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Editors and Affiliations

  1. School of Automation, Huazhong University of Science and Technology, Wuhan, China

    Cheng He

  2. Automation College, Harbin Engineering University, Harbin, China

    Hongwei Mo

  3. School of Automation, Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  4. Automation College, Harbin Engineering University, Harbin, China

    Yuxin Zhao

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Zhou, K., Zou, Y., Wang, H., Xu, G., Guo, S. (2017). Multi-objective Optimization for Ladle Tracking of Aluminium Tapping Based on NSGA-II. In: He, C., Mo, H., Pan, L., Zhao, Y. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2017. Communications in Computer and Information Science, vol 791. Springer, Singapore. https://doi.org/10.1007/978-981-10-7179-9_19

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  • DOI: https://doi.org/10.1007/978-981-10-7179-9_19

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7178-2

  • Online ISBN: 978-981-10-7179-9

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