SGEO: Equalization Optimizer with Hybrid Learning Strategies for UAV Path Planning in Complex 3D Environments

SGEO: Equalization Optimizer with Hybrid Learning Strategies for UAV Path Planning in Complex 3D Environments | 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 SGEO: Equalization Optimizer with Hybrid Learning Strategies for UAV Path Planning in Complex 3D Environments Meng Zheng, Qing He, QingNi He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3945676/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 Unmanned Aerial Vehicle (UAV) route planning is an intricate issue that requires the comprehensive consideration of multiple factors and the combination of suitable strategies to achieve efficient and safe flight paths. Its purpose is to plan a suitable navigational path for the drone to achieve a specific task or avoid obstacles. In practical applications, UAVs are usually required to accomplish tasks in a variety of complex environments, resulting in the feasible flight paths will be reduced and path planning for UAVs becomes difficult. Accordingly, we design a simple yet useful planner, called Self-adaptive Golden Equilibrium Optimization algorithm (SGEO). The proposed algorithm combines the new self-adaptive approach, the acceleration control factor and the Golden-SA strategy in order to balance the exploitation and exploration capabilities. The novel self-adaptive strategy is used to expand the global search range of the algorithm and enhance its ability for global exploration; the acceleration control factor is introduced to control the parameters a1 and a2 and improve the convergence ability of the algorithm; the Golden-SA strategy helps the candidate particles achieve better balance between different dimensions based on the golden ratio and sine function, this enables the particles to explore the search space more comprehensively. The limitations in UAV route planning are translated into the objective function, and four more algorithms are introduced to compare with SGEO in two different circumstances. The simulation findings reveal that SGEO excels at three-dimensional path planning for UAV in complicated environments. Physical sciences/Engineering/Aerospace engineering Physical sciences/Mathematics and computing/Information technology 3D environment UAV trajectory planning Equilibrium optimization algorithm Self-adaptive strategy Acceleration function control factor Golden-SA Full Text Additional Declarations No competing interests reported. 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3945676","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":279089700,"identity":"9397e742-09b5-4f51-92a2-0ac86bfe99ce","order_by":0,"name":"Meng Zheng","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Zheng","suffix":""},{"id":279089701,"identity":"0ff5a635-d3e7-42df-9bcc-3fd621184301","order_by":1,"name":"Qing He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYDACCQbGBxBWAvFamA1I1sImQZoWvtvNxyq//DnMwM+eY8DwcwcRWiTvHEu7LcNzmEGy540BY+8ZIrQY3Mgxuy0hcRjEMGBmbCNKS/63YgmDwwz2JGjJYWP8kAC0RYJYLZI30oylGQ6k80iceVZwsJcYLXw3kh9+/PHHWo6/PXnjg5/EaGE4wMDAzMPAwANlE6mF8QdxSkfBKBgFo2CkAgCB7TXTsNMS/gAAAABJRU5ErkJggg==","orcid":"","institution":"Guizhou University","correspondingAuthor":true,"prefix":"","firstName":"Qing","middleName":"","lastName":"He","suffix":""},{"id":279089702,"identity":"1762d5e2-3a01-4cb1-ba44-9a9a80bca6cf","order_by":2,"name":"QingNi He","email":"","orcid":"","institution":"Wuhan Muzhitong Technology Co.,Ltd.","correspondingAuthor":false,"prefix":"","firstName":"QingNi","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2024-02-10 10:59:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3945676/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3945676/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56917757,"identity":"7338487c-5426-4f6e-8eab-077693148e2e","added_by":"auto","created_at":"2024-05-22 06:47:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2080538,"visible":true,"origin":"","legend":"","description":"","filename":"SGEOEqualizationOptimizerwithHybridLearning.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3945676/v1_covered_fdc67431-06de-4c31-87e7-d4b174918e1d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"SGEO: Equalization Optimizer with Hybrid Learning Strategies for UAV Path Planning in Complex 3D Environments","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"3D environment, UAV trajectory planning, Equilibrium optimization algorithm, Self-adaptive strategy, Acceleration function control factor, Golden-SA","lastPublishedDoi":"10.21203/rs.3.rs-3945676/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3945676/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Unmanned Aerial Vehicle (UAV) route planning is an intricate issue that requires the comprehensive consideration of multiple factors and the combination of suitable strategies to achieve efficient and safe flight paths. 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