{"paper_id":"02304104-e17f-4bae-b32c-381d2821d5dc","body_text":"A novel meta-heuristic algorithm for high-dimensional problems: Rhinopithecus Swarm 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 A novel meta-heuristic algorithm for high-dimensional problems: Rhinopithecus Swarm Optimization Guoyuan Zhou, Dong Wang, Guoao Zhou, Jiaxuan Du, Jia Guo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3823064/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract This paper introduces a novel meta-heuristic algorithm know as Rhinopithe-cus Swarm Optimization (RSO) to deal with the high-dimensional problems. The RSO is inspired by the different social behaviors of different subgroups in the rhinopithecus swarm. This algorithm categorizes the population into king, matures, adolescences and infancy based on fitness values before each iteration. At iterations they are given different search methods including vertical migration , concerted search and mimicry due to their social division of labor. The experiment was independently conducted 36 times on the highest dimension recommended by CEC2017. The results show that comprehensive performance of RSO is higher than 8 well-known meta-heuristic algorithms including DBO, BWO, SSA, AVOA, WOA, ARBBPSO, GTO, and HHO. RSO ranked first in the CEC2017 with a score of 1.655 on 29 benchmark functions using Ferideman test, which is 42.8% better than the second-ranked algorithm SSA. Overall, it 1 is concluded that RSO outperforms well-known algorithms and can better solve high-dimensional optimization problems Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology high-dimensional problems RSO meta-heuristic algorithm Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 May, 2024 Reviews received at journal 02 May, 2024 Reviewers agreed at journal 02 May, 2024 Reviewers agreed at journal 02 May, 2024 Reviews received at journal 19 Mar, 2024 Reviewers agreed at journal 15 Mar, 2024 Reviewers agreed at journal 15 Mar, 2024 Reviewers invited by journal 15 Jan, 2024 Editor assigned by journal 15 Jan, 2024 Editor invited by journal 02 Jan, 2024 Submission checks completed at journal 01 Jan, 2024 First submitted to journal 29 Dec, 2023 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-3823064\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":264727467,\"identity\":\"4bd0ee58-e2a1-4f82-8926-33bdcc6994b0\",\"order_by\":0,\"name\":\"Guoyuan Zhou\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Huazhong Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Guoyuan\",\"middleName\":\"\",\"lastName\":\"Zhou\",\"suffix\":\"\"},{\"id\":264727468,\"identity\":\"c3d9e94a-8326-4348-8be0-016f18e22257\",\"order_by\":1,\"name\":\"Dong 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