Nash game based efficient global optimization for large-scale design problems

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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Knowledge-Assisted Optimization for Large-Scale Design Problems: A Review and Proposition

Journal of Mechanical Design ◽

10.1115/1.4044525 ◽

2019 ◽

Vol 142 (1) ◽

Cited By ~ 1

Author(s):

Di Wu ◽

G. Gary Wang

Keyword(s):

Large Scale ◽

Optimization Problems ◽

Optimization Methods ◽

Future Research ◽

Large Scale Optimization ◽

Design Problems ◽

Design Engineers ◽

Potential Applications ◽

Scale Optimization ◽

Scale Design

Abstract Practicing design engineers often have certain knowledge about a design problem. However, in the last decades, the design optimization community largely treats design functions as black-boxes. This paper discusses whether and how knowledge can help with optimization, especially for large-scale optimization problems. Existing large-scale optimization methods based on black-box functions are first reviewed, and the drawbacks of those methods are briefly discussed. To understand what knowledge is and what kinds of knowledge can be obtained and applied in a design, the concepts of knowledge in both artificial intelligence (AI) and in the area of the product design are reviewed. Existing applications of knowledge in optimization are reviewed and categorized. Potential applications of knowledge for optimization are discussed in more detail, in hope to identify possible directions for future research in knowledge-assisted optimization (KAO).

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Multi-Fidelity Multi-Objective Efficient Global Optimization Applied to Airfoil Design Problems

Applied Sciences ◽

10.3390/app7121318 ◽

2017 ◽

Vol 7 (12) ◽

pp. 1318 ◽

Cited By ~ 6

Author(s):

Atthaphon Ariyarit ◽

Masahiro Kanazaki

Keyword(s):

Global Optimization ◽

Efficient Global Optimization ◽

Design Problems ◽

Multi Objective

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Dissecting the stability determinants of a challenging de novo protein fold using massively parallel design and experimentation

10.1101/2021.12.17.472837 ◽

2021 ◽

Author(s):

Tae-Eun Kim ◽

Kotaro Tsuboyama ◽

Scott Houliston ◽

Cydney M. Martell ◽

Claire M. Phoumyvong ◽

...

Keyword(s):

Protein Design ◽

Large Scale ◽

De Novo ◽

Protein Structures ◽

Design Problems ◽

Hydrogen Deuterium ◽

The Stability ◽

Universal Stability ◽

Scale Design ◽

New Protein

Designing entirely new protein structures remains challenging because we do not fully understand the biophysical determinants of folding stability. Yet some protein folds are easier to design than others. Previous work identified the 43-residue αββ&#945 fold as especially challenging: the best designs had only a 2% success rate, compared to 39-87% success for other simple folds (1). This suggested the αββ&#945 fold would be a useful model system for gaining a deeper understanding of folding stability determinants and for testing new protein design methods. Here, we designed over ten thousand new αββ&#945 proteins and found over three thousand of them to fold into stable structures using a high-throughput protease-based assay. Nuclear magnetic resonance, hydrogen-deuterium exchange, circular dichroism, deep mutational scanning, and scrambled sequence control experiments indicated that our stable designs fold into their designed αββ&#945 structures with exceptional stability for their small size. Our large dataset enabled us to quantify the influence of universal stability determinants including nonpolar burial, helix capping, and buried unsatisfied polar atoms, as well as stability determinants unique to the αββ&#945 topology. Our work demonstrates how large-scale design and test cycles can solve challenging design problems while illuminating the biophysical determinants of folding.

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