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

[+] Author and Article Information
George Cheng

Product Design and Optimization Laboratory (PDOL), Simon Fraser University, Surrey, BC, Canada
ghc2@sfu.ca

Timothy Gjernes

Hevvy/Toyo Pumps North America Corporation, Coquitlam, BC, Canada
timgjernes@hevvypumps.com

Gary Wang

Product Design and Optimization Laboratory (PDOL), Simon Fraser University, Surrey, BC, Canada
gwa5@sfu.ca

1Corresponding author.

ASME doi:10.1115/1.4040485 History: Received November 23, 2017; Revised May 29, 2018

Abstract

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.

Copyright (c) 2018 by ASME
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