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Research Papers: Design Automation

An Adaptive Aggregation-Based Approach for Expensively Constrained Black-Box Optimization Problems

[+] Author and Article Information
George H. Cheng

Product Design and Optimization Laboratory
(PDOL),
Simon Fraser University,
Surrey, BC V3T 0A3, Canada
e-mail: ghc2@sfu.ca

Timothy Gjernes

Hevvy/Toyo Pumps North America Corporation,
Coquitlam, BC V3K 7C1, Canada

G. Gary Wang

Product Design and Optimization Laboratory
(PDOL),
Simon Fraser University,
Surrey, BC V3T 0A3, Canada
e-mail: gwa5@sfu.ca

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received November 23, 2017; final manuscript received May 29, 2018; published online June 26, 2018. Editor: Wei Chen.

J. Mech. Des 140(9), 091402 (Jun 26, 2018) (14 pages) Paper No: MD-17-1787; doi: 10.1115/1.4040485 History: Received November 23, 2017; Revised May 29, 2018

Expensive constraints are commonly seen in real-world engineering design. However, metamodel based design optimization (MBDO) approaches often assume inexpensive constraints. In this work, the situational adaptive Kreisselmeier and Steinhauser (SAKS) method was employed in the development of a hybrid adaptive aggregation-based constraint handling strategy for expensive black-box constraint functions. The SAKS method is a novel approach that hybridizes the modeling and aggregation of expensive constraints and adds an adaptive strategy to control the level of hybridization. The SAKS strategy was integrated with a modified trust region-based mode pursuing sampling (TRMPS) algorithm to form the SAKS-trust region optimizer (SAKS-TRO) for single-objective design optimization problems with expensive black-box objective and constraint functions. SAKS-TRO was benchmarked against five popular constrained optimizers and demonstrated superior performance on average. SAKS-TRO was also applied to optimize the design of an industrial recessed impeller.

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Figures

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Fig. 1

Kreisselmeier and Steinhauser function of two inequality constraints for increasing ρ

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Fig. 2

Constraint classification for the P116 (left), P118 (middle), and beam (right) problems (filled = independent, blank = aggregated)

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Fig. 3

Comparison of aggregation level across iterations for P106 given different n values

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Fig. 4

Comparison of aggregation level across iterations for P118 given different n values

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Fig. 5

Situational adaptive Kreisselmeier and Steinhauser-trust region optimizer flowchart

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Fig. 6

Fluid domain geometry (left) and single vane mesh (right)

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Fig. 7

Head drop comparison between KS-TRO optimum and baseline

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Fig. 8

Efficiency comparison between KS-TRO optimum and baseline

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Fig. 9

Constraint classification for impeller optimization (blue = independent, white = aggregated)

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