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


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
Your Session has timed out. Please sign back in to continue.





Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In