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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 Matérn-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.","publication_date":"2020,1,5","publication_name":"AIAA Scitech 2020 Forum","grobid_abstract_attachment_id":"82040113"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"On the Impact of Covariance Functions in Multi-Objective Bayesian Optimization for Engineering Design","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [14236250]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loswp.appleClientId = 'edu.academia.applesignon';</script><script defer="" 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In this work, we propose...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"An adaptive feasibility approach for constrained bayesian optimization with application in aircraft design","attachmentId":113497804,"attachmentType":"pdf","work_url":"https://www.academia.edu/117709065/An_adaptive_feasibility_approach_for_constrained_bayesian_optimization_with_application_in_aircraft_design","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/117709065/An_adaptive_feasibility_approach_for_constrained_bayesian_optimization_with_application_in_aircraft_design"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="75863792" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/75863792/srMO_BO_3GP_A_sequential_regularized_multi_objective_Bayesian_optimization_for_constrained_design_applications_using_an_uncertain_Pareto_classifier">srMO-BO-3GP: A sequential regularized multi-objective Bayesian optimization for constrained design applications using an uncertain Pareto classifier</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="220298067" href="https://independent.academia.edu/AnhDuyTran14">Anh Duy Tran</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Mechanical Design, 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">Bayesian optimization (BO) is an efficient and flexible global optimization framework that is applicable to a very wide range of engineering applications. To leverage the capability of the classical BO, many extensions, including multi-objective, multi-fidelity, parallelization, and latent-variable modeling, have been proposed to address the limitations of the classical BO framework. In this work, we propose a novel multi-objective BO formalism, called srMO-BO-3GP, to solve multi-objective optimization problems in a sequential setting. Three different Gaussian processes (GPs) are stacked together, where each of the GPs is assigned with a different task. The first GP is used to approximate a single-objective computed from the multi-objective definition, the second GP is used to learn the unknown constraints, and the third one is used to learn the uncertain Pareto frontier. At each iteration, a multi-objective augmented Tchebycheff function is adopted to convert multi-objective to sin...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"srMO-BO-3GP: A sequential regularized multi-objective Bayesian optimization for constrained design applications using an uncertain Pareto classifier","attachmentId":83505322,"attachmentType":"pdf","work_url":"https://www.academia.edu/75863792/srMO_BO_3GP_A_sequential_regularized_multi_objective_Bayesian_optimization_for_constrained_design_applications_using_an_uncertain_Pareto_classifier","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/75863792/srMO_BO_3GP_A_sequential_regularized_multi_objective_Bayesian_optimization_for_constrained_design_applications_using_an_uncertain_Pareto_classifier"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="91583011" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/91583011/Multi_Fidelity_Multi_Objective_Efficient_Global_Optimization_Applied_to_Airfoil_Design_Problems">Multi-Fidelity Multi-Objective Efficient Global Optimization Applied to Airfoil Design Problems</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="114190361" 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