On the Impact of Covariance Functions in Multi-Objective Bayesian Optimization for Engineering Design

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F20%3A73598638" target="_blank" >RIV/61989592:15310/20:73598638 - isvavai.cz</a>

Výsledek na webu

<a href="https://obd.upol.cz/id_publ/333178524" target="_blank" >https://obd.upol.cz/id_publ/333178524</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2514/6.2020-1867" target="_blank" >10.2514/6.2020-1867</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On the Impact of Covariance Functions in Multi-Objective Bayesian Optimization for Engineering Design

Popis výsledku v původním jazyce

Multi-objective Bayesian optimization (BO) is a highly useful class of methods that can effectively solve computationally expensive engineering design optimization problems with multiple objectives. However, the impact of covariance function, which is an important part of multi-objective BO, is rarely studied in the context of engineering optimization. We aim to shed light on this issue by performing numerical experiments on engineering design optimization problems, primarily low-fidelity problems so that we are able to statistically evaluate the performance of BO methods with various covariance functions. In this paper, we performed the study using a set of subsonic airfoil optimization cases as benchmark problems. Expected hypervolume improvement was used as the acquisition function to enrich the experimental design. Results show that the choice of the covariance function give a notable impact on the performance of multi-objective BO. In this regard, Kriging models with Matern-3/2 is the most robust method in terms of the diversity and convergence to the Pareto front that can handle problems with various complexities.

Název v anglickém jazyce

On the Impact of Covariance Functions in Multi-Objective Bayesian Optimization for Engineering Design

Popis výsledku anglicky

Multi-objective Bayesian optimization (BO) is a highly useful class of methods that can effectively solve computationally expensive engineering design optimization problems with multiple objectives. However, the impact of covariance function, which is an important part of multi-objective BO, is rarely studied in the context of engineering optimization. We aim to shed light on this issue by performing numerical experiments on engineering design optimization problems, primarily low-fidelity problems so that we are able to statistically evaluate the performance of BO methods with various covariance functions. In this paper, we performed the study using a set of subsonic airfoil optimization cases as benchmark problems. Expected hypervolume improvement was used as the acquisition function to enrich the experimental design. Results show that the choice of the covariance function give a notable impact on the performance of multi-objective BO. In this regard, Kriging models with Matern-3/2 is the most robust method in terms of the diversity and convergence to the Pareto front that can handle problems with various complexities.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

<a href="/cs/project/EF17_049%2F0008408" target="_blank" >EF17_049/0008408: Hydrodynamický design čerpadel</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

AIAA Scitech 2020 Forum

ISBN

978-1-62410-595-1

ISSN

—

e-ISSN

—

Počet stran výsledku

13

Strana od-do

1-13

Název nakladatele

American Institute of Aeronautics and Astronautics, Inc.

Místo vydání

Orlando, FL

Místo konání akce

Orlando, FL, USA

Datum konání akce

6. 1. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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