{"paper_id":"2cf50350-a3c1-4c5f-a2a2-476d1df7a628","body_text":"Interpolation guided optimization and composite grouping method for large-scale problems | 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 Interpolation guided optimization and composite grouping method for large-scale problems Yi Cheng, Haiyan Liu, Shouheng Tuo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6973486/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Large-scale global optimization (LSGO) problems pose significant challenges due to high dimensionality, complex variable interactions and unknown search landscapes. To address these challenges, an interpolation guided optimization and composite grouping method (IGOCG) is proposed. Firstly, a quadratic interpolation based search method (QI search) is designed to explore the landscape in a dimension-wise way in order to not only scan the search space but also collect the performance information of each dimension (decision variable). This QI search method can gather and extract variable improvement information of each dimension by quickly detecting superior region through progressive compression of the search space. Secondly, to make reasonable decomposition of fully non-separable large-scale problems, a composite grouping method is proposed. To make the decomposition more adaptable and diverse, four distinct grouping (decomposing) strategies are designed which are quartile-based hierarchical grouping, clustering-based hybrid grouping, variability grouping, and extended grouping. By decomposing large-scale problems, the search space and optimization complexity is reduced, thus better results can be achieved more efficiently. Extensive experiments are conducted on two widely used benchmark suites and comparisons are made with state-of-the-art LSGO algorithms. The results demonstrate that the proposed algorithm achieves competitive performance, validating its effectiveness and efficiency in solving large-scale problems. Large-scale optimization Problem decomposition Quadratic interpolation search Composite grouping Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Oct, 2025 Reviews received at journal 10 Oct, 2025 Reviewers agreed at journal 01 Oct, 2025 Reviewers agreed at journal 08 Aug, 2025 Reviews received at journal 04 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers invited by journal 04 Aug, 2025 Editor assigned by journal 04 Aug, 2025 Submission checks completed at journal 04 Jul, 2025 First submitted to journal 25 Jun, 2025 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. 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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-6973486\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":496553243,\"identity\":\"e39337ec-11e8-432a-9e1a-0fef2e4ae59e\",\"order_by\":0,\"name\":\"Yi Cheng\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Xi'an University of Posts and Telecommunications\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yi\",\"middleName\":\"\",\"lastName\":\"Cheng\",\"suffix\":\"\"},{\"id\":496553244,\"identity\":\"b92b2f57-9e7d-4368-b2a5-1ab056fbc569\",\"order_by\":1,\"name\":\"Haiyan Liu\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACPmYGNgYGAyCLvQEqdICAFja4Fh6Q0gRitIARCEgkEKuFncfswY+Cw/Lmkm/MpAt/MMjx3Uhg/FyA12E85oY9BocNd85OS5OekcBgLHkjgVl6Bn4tZhI8BocZN9xOPibNk8CQuOFGAlCQgBbJPwaH7TfcPNgG0lJPlBZpoC1Aw5nBtiQYENbCViYtY5CevOFMWrL1jDQJw5lnHjZL49PCz394m+SbP9a2G46fMbxdYGMjz3c8+eBnfFqgoBlMMgNjB0gxNhDWwMBQB9MyCkbBKBgFowATAAAfTkF27LkrtwAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Xi'an University of Posts and Telecommunications\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Haiyan\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"},{\"id\":496553245,\"identity\":\"d6a778ce-ec46-4c9f-bf69-0ea14901e5d5\",\"order_by\":2,\"name\":\"Shouheng Tuo\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Xi'an University of Posts and Telecommunications\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Shouheng\",\"middleName\":\"\",\"lastName\":\"Tuo\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-06-25 10:23:05\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6973486/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6973486/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":88515398,\"identity\":\"3da8f83a-8ddc-459c-9ea9-3d1b47bdaab7\",\"added_by\":\"auto\",\"created_at\":\"2025-08-07 08:49:57\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":564381,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Interpolationguidedoptimizationandcompositegroupingmethodforlargescaleproblems.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6973486/v1_covered_f419c85d-ae6e-4b5d-ae8a-15712bb0c696.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Interpolation guided optimization and composite grouping method for large-scale problems\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":true,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":true,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"cluster-computing\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [Cluster Computing](https://www.springer.com/journal/10586)\",\"snPcode\":\"10586\",\"submissionUrl\":\"https://submission.nature.com/new-submission/10586/3\",\"title\":\"Cluster Computing\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Large-scale optimization, Problem decomposition, Quadratic interpolation search, Composite grouping\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6973486/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6973486/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eLarge-scale global optimization (LSGO) problems pose significant challenges due to high dimensionality, complex variable interactions and unknown search landscapes. To address these challenges, an interpolation guided optimization and composite grouping method (IGOCG) is proposed. Firstly, a quadratic interpolation based search method (QI search) is designed to explore the landscape in a dimension-wise way in order to not only scan the search space but also collect the performance information of each dimension (decision variable). This QI search method can gather and extract variable improvement information of each dimension by quickly detecting superior region through progressive compression of the search space. Secondly, to make reasonable decomposition of fully non-separable large-scale problems, a composite grouping method is proposed. To make the decomposition more adaptable and diverse, four distinct grouping (decomposing) strategies are designed which are quartile-based hierarchical grouping, clustering-based hybrid grouping, variability grouping, and extended grouping. 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