Newton Downhill Optimizer for Global Optimization

Newton Downhill Optimizer for Global Optimization | 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 Article Newton Downhill Optimizer for Global Optimization Wanting Xiao, Kaichen Ouyang, Junbo Jacob Lian, Shaowei Gu, Yuanjun Liu, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5550160/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 The study presents the Newton's Downhill Optimizer (NDO), a novel metaheuristic algorithm designed to address the challenges of complex, high-dimensional, and nonlinear optimization problems. Mathematical-Based Algorithms (MBAs) are a category of algorithms designed based on mathematical principles. They are widely applied in numerical computation, symbolic manipulation, geometric processing, optimization problems, and probabilistic statistics, offering efficient and precise solutions to complex problems. Inspired by Newton's Method, NDO combines its precision with a downhill strategy based on stochastic processes, specifically designed to address real-world applications and benchmark problems. NDO combines the precision of Newton's method with a downhill strategy inspired by stochastic processes, enhancing the capability of exploring the solution space and escaping local optima. In benchmark tests, NDO demonstrated exceptional performance, surpassing the majority of competing algorithms in multiple test suites of CEC 2017 and CEC 2022. We conducted a comprehensive comparison of NDO against 14 well-established optimization algorithms. These include mathematical-based approaches such as AOA, SCHO, SCA, SABO, NRBO, and RUN. We also compared it with classical algorithms like CMA-ES, ABC, DE, and PSO. Additionally, we included advanced and recently published algorithms such as WSO, EHO, FDB_AGDEand GQPSO. The results demonstrate that NDO outperforms most of these algorithms. It exhibits superior convergence speed and remarkable stability.In engineering applications, NDO outperformed other algorithms in the speed reducer design task and step-cone pulley task and delivered outstanding results in multiple disk clutch brake design tasks. A significant contribution of the study is the application of NDO to breast cancer feature selection, tested on two Breast cancer datasets. The NDO demonstrated outstanding performance in accuracy, sensitivity, specificity, and the Matthews Correlation Coefficient (MCC), achieving superior accuracy across two datasets. This underscores its potential as a viable tool for addressing complex challenges in both engineering and medical fields. The source codes of NDO algorithm will be shared at https://github.com/oykc1234/NDO . Physical sciences/Mathematics and computing/Applied mathematics Physical sciences/Mathematics and computing/Computational science Newton Downhill Optimizer Feature selection Medical applications Engineering design problems. Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.zip 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. 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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-5550160","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":436389301,"identity":"b7a36c12-85ee-42b1-baac-e36894a787b3","order_by":0,"name":"Wanting Xiao","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wanting","middleName":"","lastName":"Xiao","suffix":""},{"id":436389302,"identity":"558050a3-100f-464c-a5d0-2947eb3b6a15","order_by":1,"name":"Kaichen 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problems.","lastPublishedDoi":"10.21203/rs.3.rs-5550160/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5550160/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study presents the Newton's Downhill Optimizer (NDO), a novel metaheuristic algorithm designed to address the challenges of complex, high-dimensional, and nonlinear optimization problems. Mathematical-Based Algorithms (MBAs) are a category of algorithms designed based on mathematical principles. They are widely applied in numerical computation, symbolic manipulation, geometric processing, optimization problems, and probabilistic statistics, offering efficient and precise solutions to complex problems. Inspired by Newton's Method, NDO combines its precision with a downhill strategy based on stochastic processes, specifically designed to address real-world applications and benchmark problems. NDO combines the precision of Newton's method with a downhill strategy inspired by stochastic processes, enhancing the capability of exploring the solution space and escaping local optima. In benchmark tests, NDO demonstrated exceptional performance, surpassing the majority of competing algorithms in multiple test suites of CEC 2017 and CEC 2022. We conducted a comprehensive comparison of NDO against 14 well-established optimization algorithms. These include mathematical-based approaches such as AOA, SCHO, SCA, SABO, NRBO, and RUN. We also compared it with classical algorithms like CMA-ES, ABC, DE, and PSO. Additionally, we included advanced and recently published algorithms such as WSO, EHO, FDB_AGDEand GQPSO. The results demonstrate that NDO outperforms most of these algorithms. It exhibits superior convergence speed and remarkable stability.In engineering applications, NDO outperformed other algorithms in the speed reducer design task and step-cone pulley task and delivered outstanding results in multiple disk clutch brake design tasks. A significant contribution of the study is the application of NDO to breast cancer feature selection, tested on two Breast cancer datasets. The NDO demonstrated outstanding performance in accuracy, sensitivity, specificity, and the Matthews Correlation Coefficient (MCC), achieving superior accuracy across two datasets. This underscores its potential as a viable tool for addressing complex challenges in both engineering and medical fields. The source codes of NDO algorithm will be shared at \u003ca href=\"https://github.com/oykc1234/NDO\"\u003ehttps://github.com/oykc1234/NDO\u003c/a\u003e.\u003c/p\u003e","manuscriptTitle":"Newton Downhill Optimizer for Global Optimization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-01 16:48:35","doi":"10.21203/rs.3.rs-5550160/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"9fff8c6c-6c3d-40ce-b90d-725b8cc88e3a","owner":[],"postedDate":"April 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46463629,"name":"Physical sciences/Mathematics and computing/Applied mathematics"},{"id":46463630,"name":"Physical sciences/Mathematics and computing/Computational science"}],"tags":[],"updatedAt":"2025-05-05T09:53:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-01 16:48:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5550160","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5550160","identity":"rs-5550160","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}