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RESEARCH PAPERS

Probabilistic Optimal Design Using Successive Surrogate Probability Density Functions

[+] Author and Article Information
R. J. Eggert

Mechanical Engineering Department, Union College, Schenectady, NY

R. W. Mayne

Department of Mechanical & Aerospace Engineering, State University of New York at Buffalo, Buffalo, NY

J. Mech. Des 115(3), 385-391 (Sep 01, 1993) (7 pages) doi:10.1115/1.2919203 History: Received February 01, 1990; Online June 02, 2008

Abstract

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.

Copyright © 1993 by The American Society of Mechanical Engineers
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