Uncertainty is inevitable in real-world engineering optimization. With an outer-inner optimization structure, most previous robust optimization (RO) approaches under interval uncertainty can become computationally intractable because the inner level must perform robust evaluation for each design alternative delivered from the outer level. This paper aims to propose an on-line Kriging metamodel-assisted variable adjustment robust optimization (OLK-VARO) to ease the computational burden of previous VARO approach.
In OLK-VARO, Kriging metamodels are constructed for replacing robust evaluations of the design alternative delivered from the outer level, reducing the nested optimization structure of previous VARO approach into a single loop optimization structure. An on-line updating mechanism is introduced in OLK-VARO to exploit the obtained data from previous iterations.
One nonlinear numerical example and two engineering cases have been used to demonstrate the applicability and efficiency of the proposed OLK-VARO approach. Results illustrate that OLK-VARO is able to obtain comparable robust optimums as to that obtained by previous VARO, while at the same time significantly reducing computational cost.
The proposed approach exhibits great capability for practical engineering design optimization problems under interval uncertainty.
The main contribution of this paper lies in the following: an OLK-VARO approach under interval uncertainty is proposed, which can significantly ease the computational burden of previous VARO approach.
This research has been supported by the National Natural Science Foundation of China (NSFC) under Grant No. 51505163, No. 51421062 and No. 51323009; National Basic Research Program (973 Program) of China under grant No. 2014CB046703 and the Fundamental Research Funds for the Central Universities, HUST: Grant No. 2016YXMS272.
Zhou, Q., Jiang, P., Shao, X., Zhou, H. and Hu, J. (2017), "An on-line Kriging metamodel assisted robust optimization approach under interval uncertainty", Engineering Computations, Vol. 34 No. 2, pp. 420-446. https://doi.org/10.1108/EC-01-2016-0020
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