Tabu efficient global optimization with applications in additive manufacturing

Methods based on Gaussian stochastic process (GSP) models and expected improvement (EI) functions have been promising for box-constrained expensive optimization problems. These include robust design problems with environmental variables having set-type constraints. However, the methods that combine...

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Bibliographic Details
Published in:Structural and multidisciplinary optimization 2021-06, Vol.63 (6), p.2811-2833
Main Authors: Wang, Long, Allen, Theodore T., Groeber, Michael A.
Format: Article
Language:English
Subjects:
Additive manufacturing
Algorithms
Computational Mathematics and Numerical Analysis
Constraint modelling
Design optimization
Engineering
Engineering Design
Gaussian process
Global optimization
Optimization
Process parameters
Production methods
Research Paper
Robust design
Stochastic processes
Theoretical and Applied Mechanics
Thermal simulation
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Summary:Methods based on Gaussian stochastic process (GSP) models and expected improvement (EI) functions have been promising for box-constrained expensive optimization problems. These include robust design problems with environmental variables having set-type constraints. However, the methods that combine GSP and EI sub-optimizations suffer from the following problem, which limits their computational performance. Efficient global optimization (EGO) methods often repeat the same or nearly the same experimental points. We present a novel EGO-type constraint-handling method that maintains a so-called tabu list to avoid past points. Our method includes two types of penalties for the key “infill” optimization, which selects the next test runs. We benchmark our tabu EGO algorithm with five alternative approaches, including DIRECT methods using nine test problems and two engineering examples. The engineering examples are based on additive manufacturing process parameter optimization informed using point-based thermal simulations and robust-type quality constraints. Our test problems span unconstrained, simply constrained, and robust constrained problems. The comparative results imply that tabu EGO offers very promising computational performance for all types of black-box optimization in terms of convergence speed and the quality of the final solution.
ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-021-02843-0