This paper addresses the critical issue of effectiveness and efficiency in simulation-based optimization using surrogate models as predictive models in engineering design. Specifically, it presents a novel clustering-based multilocation search (CMLS) procedure to iteratively improve the fidelity and efficacy of Kriging models in the context of design decisions. The application of this approach will overcome the potential drawback in surrogate-model-based design optimization, namely, the use of surrogate models may result in suboptimal solutions due to the possible smoothing out of the global optimal point if the sampling scheme fails to capture the critical points of interest with enough fidelity or clarity. The paper details how the problem of smoothing out the best (SOB) can remain unsolved in multimodal systems, even if a sequential model updating strategy has been employed, and lead to erroneous outcomes. Alternatively, to overcome the problem of SOB defect, this paper presents the CMLS method that uses a novel clustering-based methodical procedure to screen out distinct potential optimal points for subsequent model validation and updating from a design decision perspective. It is embedded within a genetic algorithm setup to capture the buried, transient, yet inherent data pattern in the design evolution based on the principles of data mining, which are then used to improve the overall performance and effectiveness of surrogate-model-based design optimization. Four illustrative case studies, including a truss problem, are detailed to demonstrate the application of the CMLS methodology and the results are discussed.
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e-mail: skrishna@ecs.umass.edu
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April 2008
Research Papers
A Clustering-Based Surrogate Model Updating Approach to Simulation-Based Engineering Design
Tiefu Shao,
Tiefu Shao
Graduate Research Assistant
Department of Mechanical and Industrial Engineering,
University of Massachusetts Amherst
, Amherst, MA 01003
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Sundar Krishnamurty
Sundar Krishnamurty
Associate Professor
Department of Mechanical and Industrial Engineering,
e-mail: skrishna@ecs.umass.edu
University of Massachusetts Amherst
, Amherst, MA 01003
Search for other works by this author on:
Tiefu Shao
Graduate Research Assistant
Department of Mechanical and Industrial Engineering,
University of Massachusetts Amherst
, Amherst, MA 01003
Sundar Krishnamurty
Associate Professor
Department of Mechanical and Industrial Engineering,
University of Massachusetts Amherst
, Amherst, MA 01003e-mail: skrishna@ecs.umass.edu
J. Mech. Des. Apr 2008, 130(4): 041101 (13 pages)
Published Online: February 28, 2008
Article history
Received:
November 16, 2006
Revised:
September 10, 2007
Published:
February 28, 2008
Citation
Shao, T., and Krishnamurty, S. (February 28, 2008). "A Clustering-Based Surrogate Model Updating Approach to Simulation-Based Engineering Design." ASME. J. Mech. Des. April 2008; 130(4): 041101. https://doi.org/10.1115/1.2838329
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