0
Technical Briefs

Metamodel-Based Optimization for Problems With Expensive Objective and Constraint Functions

[+] Author and Article Information
Moslem Kazemi1

School of Engineering Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canadamoslemk@sfu.ca

G. Gary Wang

School of Engineering Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canadagary_wang@sfu.ca

Shahryar Rahnamayan

Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, Oshawa, ON, L1H 7K4, Canadashahryar.rahnamayan@uoit.ca

Kamal Gupta

School of Engineering Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canadakamal@cs.sfu.ca

Throughout this paper, cheap samples refer to those points evaluated by a spline approximation of the objective function, while expensive samples denote the points evaluated by the objective/constraint function itself, which is usually more time consuming.

1

Corresponding author.

J. Mech. Des 133(1), 014505 (Jan 10, 2011) (7 pages) doi:10.1115/1.4003035 History: Received April 30, 2010; Revised November 04, 2010; Published January 10, 2011; Online January 10, 2011

Current metamodel-based design optimization methods rarely deal with problems of not only expensive objective functions but also expensive constraints. In this work, we propose a novel metamodel-based optimization method, which aims directly at reducing the number of evaluations for both objective function and constraints. The proposed method builds on existing mode pursuing sampling method and incorporates two intriguing strategies: (1) generating more sample points in the neighborhood of the promising regions, and (2) biasing the generation of sample points toward feasible regions determined by the constraints. The former is attained by a discriminative sampling strategy, which systematically generates more sample points in the neighborhood of the promising regions while statistically covering the entire space, and the latter is fulfilled by utilizing the information adaptively obtained about the constraints. As verified through a number of test benchmarks and design problems, the above two coupled strategies result in significantly low number of objective function evaluations and constraint checks and demonstrate superior performance compared with similar methods in the literature. To the best of our knowledge, this is the first metamodel-based global optimization method, which directly aims at reducing the number of evaluations for both objective function and constraints.

FIGURES IN THIS ARTICLE
<>
Copyright © 2011 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Figure 1

A tension/compression coil spring

Grahic Jump Location
Figure 2

A pressure vessel

Tables

Errata

Discussions

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