PROBABILISTIC OPTIMAL-DESIGN USING SUCCESSIVE SURROGATE PROBABILITY DENSITY-FUNCTIONS

被引：16

作者：

EGGERT, RJ

MAYNE, RW

机构：

[1] Mechanical Engineering Department, Union College, Schenectady, NY

[2] Department of Mechanical and Aerospace Engineering, State University of New York at Buffalo, Buffalo, NY

来源：

JOURNAL OF MECHANICAL DESIGN | 1993年 / 115卷 / 03期

关键词：

D O I：

10.1115/1.2919203

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Probabilistic optimization using the moment matching method and the simulation optimization method are discussed and compared to conventional deterministic optimization. A new approach based on successively approximating probability density functions, using recursive quadratic programming for the optimization process, is described. This approach incorporates the speed and robustness of analytical probability density functions and improves accuracy by considering simulation results. Theoretical considerations and an example problem illustrate the features of the approach. The paper closes with a discussion of an objective function formulation which includes the expected cost of design constraint failure.

引用

页码：385 / 391

页数：7

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