Multi-strategy integrated gorilla troops optimizer for solving global optimization and engineering design problems

Multi-strategy integrated gorilla troops optimizer for solving global optimization and engineering design 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 Article Multi-strategy integrated gorilla troops optimizer for solving global optimization and engineering design problems Zhijun Teng, Liangcen Gu, Mingyang Sun, Mugang He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6811989/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 19 You are reading this latest preprint version Abstract Inspired by the intricate group dynamics of wild gorilla populations, the Artificial Gorilla Troops Optimizer (GTO) represents a novel approach in swarm intelligence. Despite its effectiveness in performing global exploration, GTO is prone to early convergence and can easily become stuck in local optima, especially when addressing optimization problems with intricate constraints and rugged search spaces. To overcome these limitations, this paper introduces the Multi-Strategy Integrated Gorilla Troops Optimizer (MSIGTO), which integrates Latin Hypercube Sampling (LHS), Lévy Flight (LF), and the Cauchy Inverse Cumulative Distribution Operator (CICDO). The diversity of the initial population is enhanced through LHS, and the exploration and convergence characteristics of the algorithm are further improved by LF and CICDO. To assess its effectiveness, a comparative analysis is conducted between MSIGTO and various advanced optimization techniques: Parrot Optimizer (PO), Fata Morgana Algorithm (FATA), Weighted Mean of Vectors (INFO), Pelican Optimization Algorithm (POA), Hunger Games Search (HGS), Linear-SHADE (L-SHADE), African Vultures Optimization Algorithm (AVOA), and the original GTO. The evaluation is conducted using the IEEE CEC2022 benchmark set and four practical engineering case studies. The experimental analysis demonstrates that MSIGTO exhibits notable advantages regarding global exploration ability, convergence efficiency, and solution robustness. Physical sciences/Mathematics and computing Physical sciences/Mathematics and computing/Applied mathematics Physical sciences/Mathematics and computing/Computational science Gorilla troops optimizer Latin Hypercube Sampling Levy flight Cauchy Inverse Cumulative Distribution Engineering problems Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 09 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 14 Jul, 2025 Reviewers agreed at journal 12 Jul, 2025 Reviewers agreed at journal 12 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviews received at journal 10 Jul, 2025 Reviews received at journal 10 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers invited by journal 10 Jul, 2025 Editor assigned by journal 25 Jun, 2025 Editor invited by journal 12 Jun, 2025 Submission checks completed at journal 11 Jun, 2025 First submitted to journal 11 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. 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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-6811989","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":484445833,"identity":"3cfe4fee-2be8-47a0-97f7-bed7147e9afd","order_by":0,"name":"Zhijun Teng","email":"","orcid":"","institution":"Northeast Electric Power University","correspondingAuthor":false,"prefix":"","firstName":"Zhijun","middleName":"","lastName":"Teng","suffix":""},{"id":484445834,"identity":"85f1d45e-f72a-47f9-8ee4-10bc37412902","order_by":1,"name":"Liangcen Gu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYHACxgMJUMaDhIoa4vTAtDAbPDhzjEgtUJpN8mELM2Hl8hEJDAce1Nyx62/vPVaR2MDGwN/enYBXi+ENoJaEY8+SZ5w5l3YjcYcMg8SZsxvwa5kB0sJ2ONlAIsfsRuIZNgYDiVxitPwDapF/Y1aQ2MZMWIu8BFBLYtthOwMJHjMGorQY8DwAauk7nCBxJsdYIuHMMR6CfpFvT2B8+OPbYXv+9jOGH39U1Mjxt/cSsOUA/wcQndgAFeDBqxxsC1SpPUGVo2AUjIJRMHIBAKQ/TbYcJmZpAAAAAElFTkSuQmCC","orcid":"","institution":"Northeast Electric Power University","correspondingAuthor":true,"prefix":"","firstName":"Liangcen","middleName":"","lastName":"Gu","suffix":""},{"id":484445835,"identity":"4c7e449d-abbf-46ac-89e2-442d9abc3b04","order_by":2,"name":"Mingyang Sun","email":"","orcid":"","institution":"China Telecom Jilin Branch","correspondingAuthor":false,"prefix":"","firstName":"Mingyang","middleName":"","lastName":"Sun","suffix":""},{"id":484445836,"identity":"00889a15-7769-44f1-877f-a40a64b34c17","order_by":3,"name":"Mugang He","email":"","orcid":"","institution":"Northeast Electric Power University","correspondingAuthor":false,"prefix":"","firstName":"Mugang","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2025-06-03 13:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6811989/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6811989/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-19427-3","type":"published","date":"2025-10-09T15:56:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":93419449,"identity":"d2faabbb-b165-43b1-9c83-9e1d883b4b99","added_by":"auto","created_at":"2025-10-13 16:00:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2903387,"visible":true,"origin":"","legend":"","description":"","filename":"Multistrategyintegratedgorillatroopsoptimizerforsolvingglobaloptimizationandengineeringdesignproblems.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6811989/v1_covered_4dd6d62b-7fa7-4fa0-913b-5155e536697b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-strategy integrated gorilla troops optimizer for solving global optimization and engineering design 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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gorilla troops optimizer, Latin Hypercube Sampling, Levy flight, Cauchy Inverse Cumulative Distribution, Engineering problems","lastPublishedDoi":"10.21203/rs.3.rs-6811989/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6811989/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInspired by the intricate group dynamics of wild gorilla populations, the Artificial Gorilla Troops Optimizer (GTO) represents a novel approach in swarm intelligence. Despite its effectiveness in performing global exploration, GTO is prone to early convergence and can easily become stuck in local optima, especially when addressing optimization problems with intricate constraints and rugged search spaces. To overcome these limitations, this paper introduces the Multi-Strategy Integrated Gorilla Troops Optimizer (MSIGTO), which integrates Latin Hypercube Sampling (LHS), L\u0026eacute;vy Flight (LF), and the Cauchy Inverse Cumulative Distribution Operator (CICDO). The diversity of the initial population is enhanced through LHS, and the exploration and convergence characteristics of the algorithm are further improved by LF and CICDO. 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