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Using Interactive Multiobjective Optimization in Continuous Casting of Steel

机译：交互式多目标优化在钢水连铸中的应用

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We discuss some pros and cons of using different types of multiobjective optimization methods for demanding real-life problems like continuous casting of steel. In particular, we compare evolutionary approaches that are used for approximating the set of Pareto-optimal solutions to interactive methods where a decision maker actively takes part and can direct the solution process to such Pareto-optimal solutions that are interesting to her/him. Among the latter type of methods, we describe an interactive classification-based multiobjective optimization method: NIMBUS. NIMBUS converts the original objective functions together with preference information coming from the decision maker into scalar-valued optimization problems. These problems can be solved using any appropriate underlying solvers, like evolutionary algorithms. We also introduce an implementation of NIMBUS, called IND-NIMBUS, for solving demanding multiobjective optimization problems defined with different modelling and simulation tools. We apply NIMBUS and IND-NIMBUS in an optimal control problem related to the secondary cooling process in the continuous casting of steel. As an underlying solver we use a real-coded genetic algorithm. The aim in our problem is to find a control resulting with steel of the best possible quality, that is, minimizing the defects in the final product. Since the constraints describing technological and metallurgical requirements are so conflicting that they form an empty feasible set, we formulate the problem as a multiobjective optimization problem where constraint violations are minimized.
机译：我们讨论了使用不同类型的多目标优化方法来解决诸如连铸钢之类的现实生活中的问题的利弊。特别是，我们将用于逼近一组Pareto最优解的进化方法与决策者积极参与的交互式方法进行比较，并将决策过程引向她/他感兴趣的Pareto最优解。在后一种方法中，我们描述了一种基于交互式分类的多目标优化方法：NIMBUS。 NIMBUS将原始目标函数与决策者的偏好信息一起转换为标量值优化问题。可以使用任何适当的基础求解器（如进化算法）来解决这些问题。我们还介绍了一种称为IND-NIMBUS的NIMBUS实现，用于解决使用不同的建模和仿真工具定义的苛刻的多目标优化问题。我们将NIMBUS和IND-NIMBUS用于与钢连续铸造中的二次冷却过程有关的最佳控制问题。作为基础求解器，我们使用实数编码遗传算法。我们的问题的目的是找到一种采用尽可能最佳质量的钢制成的控制，即最大程度地减少最终产品中的缺陷。由于描述技术和冶金要求的约束是如此冲突，以至于它们形成了一个空的可行集，因此我们将该问题表述为一个多目标优化问题，其中约束违规被最小化。

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