Research Papers

Advanced Robust Optimization With Interval Uncertainty Using a Single-Looped Structure and Sequential Quadratic Programming

[+] Author and Article Information
Mian Li

e-mail: mianli@sjtu.edu.cn
University of Michigan-Shanghai Jiao Tong
University Joint Institute,
Shanghai Jiao Tong University,
Shanghai 200240, China

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received April 10, 2013; final manuscript received October 11, 2013; published online December 11, 2013. Assoc. Editor: Michael Kokkolaras.

J. Mech. Des 136(2), 021008 (Dec 11, 2013) (11 pages) Paper No: MD-13-1159; doi: 10.1115/1.4025963 History: Received April 10, 2013; Revised October 11, 2013

Uncertainty is inevitable and has to be taken into consideration in engineering optimization; otherwise, the obtained optimal solution may become infeasible or its performance can degrade significantly. Robust optimization (RO) approaches have been proposed to deal with this issue. Most existing RO algorithms use double-looped structures in which a large amount of computational efforts have been spent in the inner loop optimization to determine the robustness of candidate solutions. In this paper, an advanced approach is presented where no optimization run is required for robustness evaluation in the inner loop. Instead, a concept of Utopian point is proposed and the corresponding maximum variable/parameter variation will be obtained just by performing matrix operations. The obtained robust optimal solution from the new approach may be conservative, but the deviation from the true robust optimal solution is small enough and acceptable given the significant improvement in the computational efficiency. Six numerical and engineering examples are tested to show the applicability and efficiency of the proposed approach, whose solutions and computational efforts are compared to those from a previously proposed double-looped approach, sequential quadratic program-robust optimization (SQP-RO).

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Grahic Jump Location
Fig. 2

Illustration of true solution and Utopian solution

Grahic Jump Location
Fig. 3

Shapes of a parabolic function

Grahic Jump Location
Fig. 4

Concave parabola for constraints

Grahic Jump Location
Fig. 5

Solution comparison of SQP-RO and A-SQP-RO: (a) Constraint contour comparison and (b) final solution comparison




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