Please use this identifier to cite or link to this item: https://doi.org/10.1080/03052150500211911
Title: A framework for design optimization using surrogates
Authors: Won, K.S. 
Ray, T.
Keywords: Cokriging
Kriging
Radial basis function
Issue Date: Oct-2005
Citation: Won, K.S., Ray, T. (2005-10). A framework for design optimization using surrogates. Engineering Optimization 37 (7) : 685-703. ScholarBank@NUS Repository. https://doi.org/10.1080/03052150500211911
Abstract: Design optimization is a computationally expensive process as it requires the assessment of numerous designs and each of such assessments may be based on expensive analyses (e.g. computational fluid dynamics method or finite element based method). One way to contain the computational time within affordable limits is to use computationally cheaper approximations (surrogates) in lieu of the actual analyses during the course of optimization. This article introduces a framework for design optimization using surrogates. The framework is built upon a stochastic, zero-order, population-based optimization algorithm, which is embedded with a modified elitism scheme, to ensure convergence in the actual function space. The accuracy of the surrogate model is maintained via periodic retraining and the number of data points required to create the surrogate model is identified by a k-means clustering algorithm. A comparison is provided between different surrogate models (Kriging, radial basis functions (Exact and Fixed) and Cokriging) using a number of mathematical test functions and engineering design optimization problems. The results clearly indicate that for a given fixed number of actual function evaluations, the surrogate assisted optimization model consistently performs better than a pure optimization model using actual function evaluations.
Source Title: Engineering Optimization
URI: http://scholarbank.nus.edu.sg/handle/10635/115557
ISSN: 0305215X
DOI: 10.1080/03052150500211911
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