Please use this identifier to cite or link to this item: https://doi.org/10.1109/TEVC.2005.859464
Title: Max-min surrogate-assisted evolutionary algorithm for robust design
Authors: Ong, Y.-S.
Nair, P.B.
Lum, K.Y. 
Keywords: Evolutionary algorithm (EA)
Function approximation and surrogate modeling
Robust design optimization
Issue Date: Aug-2006
Citation: Ong, Y.-S., Nair, P.B., Lum, K.Y. (2006-08). Max-min surrogate-assisted evolutionary algorithm for robust design. IEEE Transactions on Evolutionary Computation 10 (4) : 392-404. ScholarBank@NUS Repository. https://doi.org/10.1109/TEVC.2005.859464
Abstract: Solving design optimization problems using evolutionary algorithms has always been perceived as finding the optimal solution over the entire search space. However, the global optima may not always be the most desirable solution in many real-world engineering design problems. In practice, if the global optimal solution is very sensitive to uncertainties, for example, small changes in design variables or operating conditions, then it may not be appropriate to use this highly sensitive solution. In this paper, we focus on combining evolutionary algorithms with function approximation techniques for robust design. In particular, we investigate the application of robust genetic algorithms to problems with high dimensions. Subsequently, we present a novel evolutionary algorithm based on the combination of a max-min optimization strategy with a Baldwinian trust-region framework employing local surrogate models for reducing the computational cost associated with robust design problems. Empirical results are presented for synthetic test functions and aerodynamic shape design problems to demonstrate that the proposed algorithm converges to robust optimum designs on a limited computational budget. © 2006 IEEE.
Source Title: IEEE Transactions on Evolutionary Computation
URI: http://scholarbank.nus.edu.sg/handle/10635/111426
ISSN: 1089778X
DOI: 10.1109/TEVC.2005.859464
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