Process optimization based on high-fidelity computer simulations or real experimentation is commonly expensive. Therefore, surrogate models are frequently used to reduce the computational or experimental cost. However, surrogate models need to achieve a maximum accuracy with a limited number of sampled points. Sequential sampling is a procedure in which sequentially surrogates are fitted and each surrogate defines the points that need to be sampled and used to fit the next model. For optimization purposes, points are sampled on regions of high potential for the optimal solutions. In this work, we first compared the effect of using different initial sets of points (experimental designs) in a sequential surrogate-based multiobjective optimization method. The optimization method is tested on five benchmark problems and the performance is quantified based on the total number of function evaluations and the quality of the final Pareto Front. Then an industrial applications on titanium welding is presented to show the use of the method. The case study is based on real experimental data.

中文翻译：

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基于顺序代理的多目标优化方法：初始数据集的影响

基于高保真计算机仿真或真实实验的过程优化通常很昂贵。因此，通常使用替代模型来减少计算或实验成本。但是，代理模型需要在有限数量的采样点上实现最大的准确性。顺序采样是一个过程，在该过程中，将安装顺序替代项，每个替代项都定义了需要采样的点并用于拟合下一个模型。为了优化目的，在高电位区域上采样点以获得最佳解决方案。在这项工作中，我们首先比较了在基于替代指标的多目标优化方法中使用不同初始点集（实验设计）的效果。优化方法针对五个基准问题进行了测试，并根据功能评估的总数和最终Pareto Front的质量对性能进行了量化。然后介绍了钛焊接的工业应用，以说明该方法的使用。案例研究基于真实的实验数据。