This study proposes a novel approach based on statistical learning and multi-objective optimization to reduce the need for experiments during the design phase of new cleaning cycles for household dishwashers. We first build regression models associated with the feature selection methods to predict the outputs of a dishwasher cleaning cycle by using the existing cleaning cycles’ program flows as input data and the results of the performance laboratory tests of the related cleaning cycles as output data. Then, a multi-objective optimization problem is defined by assigning the regression models and chosen features as objective functions and unknown decision variables, respectively. Obtained optimization problem is then solved by using evolutionary algorithms according to the designer’s preferences (or customers’ needs).

中文翻译：

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基于统计建模和多目标优化的策略来设计洗碗机清洁周期

这项研究提出了一种基于统计学习和多目标优化的新颖方法，以减少家用洗碗机新清洁周期设计阶段的实验需求。我们首先构建与特征选择方法相关的回归模型，通过使用现有清洁循环的程序流作为输入数据以及相关清洁循环的性能实验室测试结果作为输出数据来预测洗碗机清洁循环的输出。然后，通过将回归模型和所选特征分别指定为目标函数和未知决策变量来定义多目标优化问题。然后根据设计者的偏好（或客户的需求）使用进化算法来解决获得的优化问题。