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Robust multi-objective optimization of rolling schedule for tandem cold rolling based on evolutionary direction differential evolution algorithm

机译:基于进化方向差分进化算法的串联冷轧鲁棒轧制多目标优化

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摘要

According to the actual requirements, profile and rolling energy consumption are selected as objective functions of rolling schedule optimization for tandem cold rolling.Because of mechanical wear, roll di-ameter has some uncertainty during the rolling process, ignoring which will cause poor robustness of rolling schedule.In order to solve this problem, a robust multi-objective optimization model of rolling schedule for tandem cold rolling was established.A differential evolution algorithm based on the evo-lutionary direction was proposed.The algorithm calculated the horizontal angle of the vector, which was used to choose mutation vector.The chosen vector contained converging direction and it changed the random mutation operation in differential evolution algorithm.Efficiency of the proposed algo-rithm was verified by two benchmarks.Meanwhile, in order to ensure that delivery thicknesses have descending order like actual rolling schedule during evolution, a modified Latin Hypercube Sampling process was proposed.Finally, the proposed algorithm was applied to the model above.Results showed that profile was improved and rolling energy consumption was reduced compared with the ac-tual rolling schedule.Meanwhile, robustness of solutions was ensured.
机译:根据实际要求,型材和滚动能耗被选为滚动时间表优化对串联冷轧的客观函数。因为机械磨损,辊Di-Ameter在轧制过程中具有一些不确定性,忽略会导致滚动较差的鲁棒性计划来解决这个问题,建立了一种稳健的多目标优化模型,用于串联冷轧的滚动时间表。提出了一种基于EVO-疏液方向的差分演化算法。算法计算了向量的水平角度,用于选择突变向量。所选择的载体包含会聚方向,它改变了差分演进算法中的随机突变操作。所提出的算法的效率是由两个基准验证的。以确保输送厚度有所下降顺序,如在演变期间的实际滚动时间表,一个改进的拉丁杂交超级SA提出了一种方法。最后,将所提出的算法应用于上述模型。结果表明,与AC-Tual Colling Scheduess.Mean的鲁劳利,确保了解决方案的提高,滚动能耗降低。

著录项

  • 来源
    《钢铁研究学报(英文版)》 |2017年第8期|795-802|共8页
  • 作者

    Yong Li; Lei Fang;

  • 作者单位

    School of Electrical Engineering,Shenyang University of Technology,Shenyang 110873,Liaoning,China;

    School of Information Science and Engineering,Shenyang University of Technology,Shenyang 110873,Liaoning,China;

  • 收录信息 中国科学引文数据库(CSCD);中国科技论文与引文数据库(CSTPCD);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
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