Simulation-driven sequential metamodels for fuzzy reliability-based optimisation tasks
Simulation-driven sequential metamodels for fuzzy reliability-based optimisation tasks
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DOI：
10.1504/ijmmno.2012.044712
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发表时间：
2012-01
期刊：
影响因子：
--
通讯作者：
S. Pannier;W. Graf
中科院分区：
文献类型：
--
作者：
S. Pannier;W. Graf
基于模糊可靠性的优化任务具有高维和高度非线性的响应面。由于计算费用的原因，必须应用能够适当地逼近这些响应面的元模型。本文介绍了两种互补的仿真驱动序贯元模型方法，用于基于模糊可靠性的优化。首先，给出了基于模糊可靠性优化的函数分解。它可以分别为设计变量空间和不确定变量空间建立元模型。其次，采用局部元模型自适应逼近响应面。因此，逐点局部逼近由基于模糊可靠性的优化算法控制。因此，仅在感兴趣的区域中执行功能评估，例如有限元分析。
Fuzzy reliability-based optimisation tasks feature high-dimensional and highly non-linear response surfaces. Due to the computational expense, metamodels have to be applied, which are capable to approximate these response surfaces appropriately. In this paper, two complementary approaches of simulation-driven sequential metamodels are introduced for a fuzzy reliability-based optimisation. First, a function decomposition for a fuzzy reliability-based optimisation is worked out. It enables to build metamodels for the space of design variables and the space of uncertain variables separately. Second, local metamodels are applied to approximate the response surface adaptively. Thereby, the pointwise local approximations are controlled by the fuzzy reliability-based optimisation algorithm. In consequence, the function evaluation, e.g., finite element analysis, is only performed in regions of interest.