An Improved Marine Predators Algorithm for Shipment Status Time Estimation and Regression Problems

An Improved Marine Predators Algorithm for Shipment Status Time Estimation and Regression 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 An Improved Marine Predators Algorithm for Shipment Status Time Estimation and Regression Problems Resul Özdemir, Murat Tasyurek, Veysel Aslantas This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5254678/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Feb, 2026 Read the published version in International Journal of Machine Learning and Cybernetics → Version 1 posted 12 You are reading this latest preprint version Abstract In the realm of machine learning, the optimization of hyperparameters is a pivotal step in creating intelligent models of high efficacy. Configuration of hyperparameters in a smart way contributes to enhanced model generalization and resilience, mitigating the risks associated with overfitting or underfitting. Optimization algorithms play a decisive role in determining the ideal hyperparameters for machine learning algorithms. This study introduces a novel hyperparameter optimization approach based on the Marine Predators Algorithm designed for regression problems. The performance of the proposed algorithm is benchmarked against a spectrum of popular and classical optimization algorithms from the literature. Furthermore, hyperparameter optimization is examined for three widely employed machine learning algorithms which are Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Support Vector Regression (SVR). Regression models are constructed using a actual world dataset for prediction of shipment status time, and the proposed algorithm is further evaluated with public three datasets suitable for regression analysis. A new hyperparameter decoding approach is proposed to turn hyperparameter optimization into a numerical optimization problem regardless of parameter type. Consequently, the experimental results indicate that the proposed optimization algorithm exhibited superior performance than the compared optimizers in hyperparameter optimization for both machine learning algorithms across all regression datasets. Machine learning Hyperparameter Optimization Marine Predators Algorithm Regression Analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Feb, 2026 Read the published version in International Journal of Machine Learning and Cybernetics → Version 1 posted Editorial decision: Revision requested 21 Nov, 2024 Reviews received at journal 14 Nov, 2024 Reviews received at journal 29 Oct, 2024 Reviews received at journal 23 Oct, 2024 Reviewers agreed at journal 19 Oct, 2024 Reviewers agreed at journal 18 Oct, 2024 Reviewers agreed at journal 18 Oct, 2024 Reviewers agreed at journal 18 Oct, 2024 Reviewers invited by journal 18 Oct, 2024 Editor assigned by journal 16 Oct, 2024 Submission checks completed at journal 15 Oct, 2024 First submitted to journal 13 Oct, 2024 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. 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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-5254678","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":366399681,"identity":"fd097e3b-c251-4259-aed2-eb8d643562e2","order_by":0,"name":"Resul Özdemir","email":"data:image/png;base64,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","orcid":"","institution":"Erciyes University","correspondingAuthor":true,"prefix":"","firstName":"Resul","middleName":"","lastName":"Özdemir","suffix":""},{"id":366399682,"identity":"a125c1c4-44ff-491f-9a32-9b48cc8cc1c2","order_by":1,"name":"Murat Tasyurek","email":"","orcid":"","institution":"Kayseri University","correspondingAuthor":false,"prefix":"","firstName":"Murat","middleName":"","lastName":"Tasyurek","suffix":""},{"id":366399683,"identity":"f4a9ad3c-7f47-4f63-ba50-8f2a1e659188","order_by":2,"name":"Veysel Aslantas","email":"","orcid":"","institution":"Erciyes University","correspondingAuthor":false,"prefix":"","firstName":"Veysel","middleName":"","lastName":"Aslantas","suffix":""}],"badges":[],"createdAt":"2024-10-13 09:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5254678/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5254678/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13042-025-02967-5","type":"published","date":"2026-02-02T15:57:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":102234024,"identity":"84161e48-bd8f-4195-a053-bac120576d25","added_by":"auto","created_at":"2026-02-09 16:04:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4532291,"visible":true,"origin":"","legend":"","description":"","filename":"Springer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5254678/v1_covered_4f5e4bcd-5d44-4a3d-bb2c-c54380e4dfad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Improved Marine Predators Algorithm for Shipment Status Time Estimation and Regression Problems","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-machine-learning-and-cybernetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmlc","sideBox":"Learn more about [International Journal of Machine Learning and Cybernetics](http://actavetscand.biomedcentral.com/)","snPcode":"13042","submissionUrl":"https://submission.nature.com/new-submission/13042/3","title":"International Journal of Machine Learning and Cybernetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Machine learning, Hyperparameter Optimization, Marine Predators Algorithm, Regression Analysis","lastPublishedDoi":"10.21203/rs.3.rs-5254678/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5254678/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the realm of machine learning, the optimization of hyperparameters is a pivotal step in creating intelligent models of high efficacy. 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