Exponential-trigonometric Optimization Algorithm with Multi-Strategy Fusion for UAV three-dimensional path planning

Exponential-trigonometric Optimization Algorithm with Multi-Strategy Fusion for UAV three-dimensional path planning | 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 Exponential-trigonometric Optimization Algorithm with Multi-Strategy Fusion for UAV three-dimensional path planning Tao Xu, Chaoyue Chen, Fanfan Meng, Dongdong Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5825357/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract With the rapid advancement of Unmanned Aerial Vehicle (UAV) technology, trajectory planning has become a focus research. This paper proposes a three-dimensional path planning method for UAV based on an improved Exponential-triangle Optimization Algorithm (IETO). By constructing a multi-objective optimization function that considers factors such as path length, flight altitude, and turning angle, a comprehensive evaluation of path quality is able to be achieved. The IETO algorithm incorporates interval-constrained logistic chaotic mapping, dynamic reverse learning strategy, and an adaptive artificial bee colony algorithm (ABC) escape mechanism within the ETO algorithm. These enhancements prevent premature convergence to local optima. Through benchmark function tests on the CEC2017 test set and simulations in peak threat environments, the IETO algorithm demonstrated superior robustness. Compared to mainstream algorithms like GWO and GJO, IETO achieves the best performance in 62% of function tests. It also demonstrates exceptional performance in solving complex functions, effectively balances exploration and exploitation capabilities. In mountainous environments, the IETO algorithm generates the smoothest paths with the lowest costs and quickly converges to the optimal solution. Exponential-Triangle Optimization Algorithm (IETO) interval-constrained logistic chaotic mapping adaptive artificial bee colony algorithm (ABC) dynamic reverse learning strategies path planning Unmanned Aerial Vehicle (UAV) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 14 Mar, 2025 Reviews received at journal 14 Mar, 2025 Reviews received at journal 13 Mar, 2025 Reviews received at journal 10 Mar, 2025 Reviews received at journal 09 Mar, 2025 Reviewers agreed at journal 26 Feb, 2025 Reviewers agreed at journal 26 Feb, 2025 Reviewers agreed at journal 25 Feb, 2025 Reviewers agreed at journal 24 Feb, 2025 Reviewers agreed at journal 23 Feb, 2025 Reviewers invited by journal 23 Feb, 2025 Editor assigned by journal 17 Jan, 2025 Submission checks completed at journal 17 Jan, 2025 First submitted to journal 14 Jan, 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. 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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-5825357","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":403621257,"identity":"580b02b4-5060-4182-bc38-6cf0f0295ec9","order_by":0,"name":"Tao Xu","email":"","orcid":"","institution":"Shenyang Aerospace University","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Xu","suffix":""},{"id":403621258,"identity":"22fe5f45-6463-41c1-bc34-c6db5ec84ec5","order_by":1,"name":"Chaoyue Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYDACCSBmbAASzMwHDnz4QZIWdrbEgzN7SNLCz2N8mIONCB38s5uPPfi5w07O4DDPh8MMPAzy/GIHCFhy51i6Ye+ZZGODw7wbDhdYMBjOnJ2AX4uBRI6ZNGMbc+IGkJYZPAwJBrcJasn/BtRSD9TC8+AwDxtRWnLYgFoOg7QwEKdF4kaamWRv23FjycNsBsBAliDsF/4Zyc8kfrZVy/GdP/z4w4cfNvL80gS0wIHCAYitRCoHAfkGEhSPglEwCkbByAIAUldFDTuh2jkAAAAASUVORK5CYII=","orcid":"","institution":"Shenyang Aerospace University","correspondingAuthor":true,"prefix":"","firstName":"Chaoyue","middleName":"","lastName":"Chen","suffix":""},{"id":403621259,"identity":"3191f7b5-7d84-466a-9db8-83559cafe0f1","order_by":2,"name":"Fanfan Meng","email":"","orcid":"","institution":"Shenyang Aerospace University","correspondingAuthor":false,"prefix":"","firstName":"Fanfan","middleName":"","lastName":"Meng","suffix":""},{"id":403621260,"identity":"42f32e17-fef7-4de0-bc78-a1622fe03891","order_by":3,"name":"Dongdong Ma","email":"","orcid":"","institution":"Shenyang Aerospace University","correspondingAuthor":false,"prefix":"","firstName":"Dongdong","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2025-01-14 08:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5825357/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5825357/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74252714,"identity":"f551f76a-b932-4529-8d7e-8cfc2d84ffbd","added_by":"auto","created_at":"2025-01-20 10:46:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5031338,"visible":true,"origin":"","legend":"","description":"","filename":"ExponentialtrigonometricOptimizationAlgorithmwithMultiStrategyFusionforUAVthreedimensionalpathplanning.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5825357/v1_covered_66137669-032a-4781-b794-320579347efe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exponential-trigonometric Optimization Algorithm with Multi-Strategy Fusion for UAV three-dimensional path planning","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":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Exponential-Triangle Optimization Algorithm (IETO), interval-constrained logistic chaotic mapping, adaptive artificial bee colony algorithm (ABC), dynamic reverse learning strategies, path planning, Unmanned Aerial Vehicle (UAV)","lastPublishedDoi":"10.21203/rs.3.rs-5825357/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5825357/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the rapid advancement of Unmanned Aerial Vehicle (UAV) technology, trajectory planning has become a focus research. 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