Abstract

The increased complexity of manufacturing systems makes the acquisition of the system performance estimate a black-box procedure (e.g., simulation tools). The efficiency of most black-box optimization algorithms is affected significantly by initial designs (populations). In most population initializers, points are spread out to explore the entire domain, e.g., space-filling designs. Some population initializers also consider exploitation procedures to speed up the optimization process. However, they are either application-dependent or require an additional budget. This article proposes a generic method to generate, without an additional budget, several good solutions in the initial design. The aim of the method is to optimize the quantile of the objective function values in the generated sample set. The proposed method is based on a clustering of the solution space; feasible solutions are clustered into groups and the budget is allocated to each group dynamically based on the observed information. The asymptotic performance of the proposed method is analyzed theoretically. The numerical results show that, if proper clustering rules are applied, an unbalanced design is generated in which promising solutions have higher sampling probabilities than non-promising solutions. The numerical results also show that the method is robust to wrong clustering rules.

摘要

制造系统复杂性的增加使得对系统性能的评估成为一个黑盒程序（例如，仿真工具）。大多数黑盒优化算法的效率都会受到初始设计（种群）的影响。在大多数人口初始化器中，点被散布以探索整个域，例如空间填充设计。一些总体初始化程序还考虑利用开发过程来加快优化过程。但是，它们要么取决于应用程序，要么需要额外的预算。本文提出了一种通用方法，无需额外预算即可在初始设计中生成多个良好的解决方案。该方法的目的是优化所生成样本集中目标函数值的分位数。所提出的方法基于解决方案空间的聚类。可行的解决方案分为几组，并根据观察到的信息将预算动态分配给每个组。从理论上分析了该方法的渐近性能。数值结果表明，如果应用适当的聚类规则，则会生成不平衡设计，其中有前途的解决方案比非有前途的解决方案具有更高的采样概率。数值结果还表明，该方法对错误的聚类规则具有鲁棒性。如果应用适当的聚类规则，则会生成不平衡的设计，其中有前途的解决方案比非有前途的解决方案具有更高的采样概率。数值结果还表明，该方法对错误的聚类规则具有鲁棒性。如果应用适当的聚类规则，则会生成不平衡的设计，其中有前途的解决方案比非有前途的解决方案具有更高的采样概率。数值结果还表明，该方法对错误的聚类规则具有鲁棒性。