Non-parametric stochastic subset optimization for optimal-reliability design problems

被引：25

作者：

Jia, Gaofeng ^{[1
]}

Taflanidis, Alexandros A. ^{[1
]}

机构：

[1] Univ Notre Dame, Dept Civil & Environm Engn & Earth Sci, Notre Dame, IN 46556 USA

来源：

COMPUTERS & STRUCTURES | 2013年 / 126卷

基金：

美国国家科学基金会;

关键词：

Stochastic subset optimization; Reliability-based optimization; Kernel density estimation; Stochastic simulation; KERNEL DENSITY-ESTIMATION; BANDWIDTH SELECTION; GROUND MOTIONS; SYSTEM-DESIGN; SIMULATION; SENSITIVITY; DAMPERS; SCHEME;

D O I：

10.1016/j.compstruc.2012.12.009

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The stochastic subset optimization (SSO) algorithm has been recently proposed for design problems that use the system reliability as objective function. It is based on simulation of samples of the design variables from an auxiliary probability density function, and uses this information to identify subsets for the optimal solution. This paper presents an extension, termed Non-Parametric SSO, that adopts kernel density estimation (KDE) to approximate the objective function through these samples. It then uses this approximation to identify candidate points for the global minimum. To reduce the computational effort an iterative approach is established whereas efficient reflection methodologies are implemented for the KDE. (C) 2012 Elsevier Ltd. All rights reserved.

引用

页码：86 / 99

页数：14

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