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Research Papers: Design Automation

Trust Region Based Mode Pursuing Sampling Method for Global Optimization of High Dimensional Design Problems

[+] Author and Article Information
George H. Cheng, Adel Younis, Kambiz Haji Hajikolaei

Product Design and Optimization Lab (PDOL),
School of Mechatronic Systems Engineering,
Simon Fraser University,
250-13450 102 Avenue,
Surrey, BC V3T0A3, Canada

G. Gary Wang

Product Design and Optimization Lab (PDOL),
School of Mechatronic Systems Engineering,
Simon Fraser University,
250-13450 102 Avenue,
Surrey, BC V3T0A3, Canada
e-mail: gary_wang@sfu.ca

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received January 16, 2014; final manuscript received November 17, 2014; published online December 17, 2014. Assoc. Editor: Christopher Mattson.

J. Mech. Des 137(2), 021407 (Feb 01, 2015) (9 pages) Paper No: MD-14-1054; doi: 10.1115/1.4029219 History: Received January 16, 2014; Revised November 17, 2014; Online December 17, 2014

Mode pursuing sampling (MPS) was developed as a global optimization algorithm for design optimization problems involving expensive black box functions. MPS has been found to be effective and efficient for design problems of low dimensionality, i.e., the number of design variables is less than 10. This work integrates the concept of trust regions into the MPS framework to create a new algorithm, trust region based mode pursuing sampling (TRMPS2), with the aim of dramatically improving performance and efficiency for high dimensional problems. TRMPS2 is benchmarked against genetic algorithm (GA), dividing rectangles (DIRECT), efficient global optimization (EGO), and MPS using a suite of standard test problems and an engineering design problem. The results show that TRMPS2 performs better on average than GA, DIRECT, EGO, and MPS for high dimensional, expensive, and black box (HEB) problems.

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Figures

Grahic Jump Location
Fig. 1

Stepped cantilever beam with d steps

Grahic Jump Location
Fig. 2

(a) 30D cantilever with 5000 points and (b) 30D cantilever problem with equivalent nfe

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