Optimisation of steel rolling schedule based on evolutionary multi-tasking transfer algorithm
研究了变速度动态轧制过程，结合现场数据建立降维迁移模型，提出基于显式迁移解策略的多目标多因子进化算法，以平衡复杂工业迭代与实时性要求，提升轧制效率与质量。
Strip rolling is an important part of steel processing. The load distribution scheme in the rolling process directly affects production efficiency and product quality. A dynamic rolling process with changing speed is investigated to improve quality and reduce energy use. To balance the iterative calculation of the complex industrial evolution process and the requirements of high real-time, this study examine the rolling process by combining the multivariate, multi-constraint, and strong coupling characteristics of field measured data. On this basis, several conflicting rolling optimisation objectives in the process of rolling schedule optimisation were analysed, and a dimension reduction migration model based on transfer component analysis was established. In case of an inefficient transfer of feasible solutions , a multi-objective multifactorial evolutionary algorithm based on an explicit transfer solution strategy (MOMFEA-ETS) was proposed. The proposed algorithm obtained four average distance optimal values for eight practical rolling programming problems.