Benchmarking constrained, multi-objective and surrogate-assisted derivative-free optimization methods

Benchmarking constrained, multi-objective and surrogate-assisted derivative-free optimization methods | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Benchmarking constrained, multi-objective and surrogate-assisted derivative-free optimization methods Charles Audet, Warren Hare, Christophe Tribes This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6657064/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Benchmarking is essential for assessing the effectiveness of optimization algorithms. This is especially true in derivative-free optimization, where target problems are often complex simulations that require extensive time to evaluate.This limits the number of evaluations that can be performed, making it critical to have a good understanding of the potential quality of various algorithms. This paper reviews standard benchmarking methods, including convergence plots, performance profiles, data profiles, and accuracy profiles, widely used to evaluate optimization algorithms. The primary contribution of this work is a formal extension of these benchmarking techniques to three specific contexts: constrained optimization, multi-objective optimization, and surrogate-based optimization. AMS subject classifications: primary 90-05, 90C56; secondary 90C29, 90-10 Benchmarking derivative-free optimization constrained optimization multi-objective optimization surrogate-based optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6657064","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":462750944,"identity":"2acdad91-b02a-4dde-ae6a-c9f58057af01","order_by":0,"name":"Charles Audet","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIie3SsUrDQBjA8a8IcQnG8USJr3AloJPta3S8cGAG0ylLhhIOhDxDBvEZDELnOwJxyQNk6FAQsgknLk7i15jS6SSj4P0h38GRHx+BANhsfzCvwLHFxwGgACnACQw3pkiLg+3sjsimtxPBfiF0MZAzgUTlY8j5fadZmgFtefn6/rjxHXIXbRnMLo3kog4K1lRIbhOq1l3gkGWJW/hUmAhhAYS5RBJfEbWuwvyHyImZRB8QfmUwb+PrT/VwIHMjWcS4RRz1W0CJAwlNhLRxAqyuXNp0CWlq/Bb3rSwY5dxEvCJ6Br3KfPrCS52uNr53vHzSOp3dmMg+dzglwCmD/k8YGxJPjn/dZrPZ/kXf0F5hvo5CQkIAAAAASUVORK5CYII=","orcid":"","institution":"Polytechnique Montréal","correspondingAuthor":true,"prefix":"","firstName":"Charles","middleName":"","lastName":"Audet","suffix":""},{"id":462750948,"identity":"925663c3-319e-4f3d-a17a-34dcb85a22de","order_by":1,"name":"Warren Hare","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Warren","middleName":"","lastName":"Hare","suffix":""},{"id":462750949,"identity":"4a03d978-5eba-4f49-8111-9a90ace11981","order_by":2,"name":"Christophe Tribes","email":"","orcid":"","institution":"Polytechnique Montréal","correspondingAuthor":false,"prefix":"","firstName":"Christophe","middleName":"","lastName":"Tribes","suffix":""}],"badges":[],"createdAt":"2025-05-13 15:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6657064/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6657064/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92671197,"identity":"698b0b16-4172-4a0c-98fb-3d829c3fb33b","added_by":"auto","created_at":"2025-10-02 18:46:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":425012,"visible":true,"origin":"","legend":"","description":"","filename":"Benchmarking.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6657064/v1_covered_211c23a5-79f7-4e26-b5bd-ccce0a43241e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Benchmarking constrained, multi-objective and surrogate-assisted derivative-free optimization methods","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Benchmarking, derivative-free optimization, constrained optimization, multi-objective optimization, surrogate-based optimization","lastPublishedDoi":"10.21203/rs.3.rs-6657064/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6657064/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBenchmarking is essential for assessing the effectiveness of optimization algorithms. \u0026nbsp;This is especially true in derivative-free optimization, where target problems are often complex simulations that require extensive time to evaluate.This limits the number of evaluations that can be performed, making it critical to have a good understanding of the potential quality of various algorithms. 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