An enhanced Pied kingfisher optimizer for UAV path planning and engineering design problems

An enhanced Pied kingfisher optimizer for UAV path planning 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 Research Article An enhanced Pied kingfisher optimizer for UAV path planning and engineering design problems Da Fang, Quan Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6357683/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 Pied Kingfisher Optimizer (PKO) is an advanced optimization algorithm. Its slow convergence and propensity to become stuck in local optima are its drawbacks, though. We suggest an Enhanced Pied Kingfisher Optimizer algorithm (EPKO) to overcome these drawbacks. In order to enhance the algorithm's exploratory position modifications and make it easier to identify the global optimum, tent mapping and an adaptive T-distribution control approach are used. Additionally, we present a Cauchy mutation method, which gives individuals a strong ability to avoid local extrema and guide the population in more advantageous directions. In order to improve the optimizer's search performance and greatly boost the algorithm's accuracy, speed, and stability for solving complicated issues, a leader-based boundary control technique is also suggested. We compare EPKO's performance against eight well-known algorithms in a number of dimensions using 29 CEC2017 benchmark functions. The efficacy of EPKO was demonstrated by the fact that our algorithm came out on top in every comparison. We also mathematically modeled the UAV and used a variety of competitor algorithms to address the UAV path planning problem in order to assess the suggested method's practicality. Additionally, we tackled three engineering design challenges using several competitor methods. The results show that EPKO has the best performance. When it comes to solution quality and stability, EPKO generally performs better than its competitors, demonstrating its greater application potential. Enhanced Pied Kingfisher Optimizer Meta-heuristic algorithm Swarm intelligence UAV path planning Engineering design problems 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-6357683","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":454825518,"identity":"a12b01e7-b10c-4175-9905-188386891e9b","order_by":0,"name":"Da Fang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBAC9gYgwcNgw2AA4TMT1sJzAKwljcGAjUQth0nRwn728Is3NecTt8s3P/zAUGGd2MB+9gB+LTx5aZZzjt1O3NnGZizBcCY9sYEnLwGvFnuGHDNjHrbbuRuO8bAxMLYdTmyQ4DHAbwv/G6CWf+egWv4Ro0Uix/gxb9sBqJYGorS8MWOc25dcv+FYmrFEwrF04zaeHEIOyzH+8OabnbHB4cMPP3yosZbtZz+DXwsQsEnAmQkgLiH1QMD8gQhFo2AUjIJRMJIBAHOhQs6kDPIvAAAAAElFTkSuQmCC","orcid":"","institution":"Intelligent manufacturing Institute，Wuhan Technical College of Communications","correspondingAuthor":true,"prefix":"","firstName":"Da","middleName":"","lastName":"Fang","suffix":""},{"id":454825520,"identity":"acc49264-dce3-4811-b1ac-8f1b994afc15","order_by":1,"name":"Quan Zhou","email":"","orcid":"","institution":"Hubei Communications Technical College","correspondingAuthor":false,"prefix":"","firstName":"Quan","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2025-04-02 05:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6357683/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6357683/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99312263,"identity":"d0f792ab-81cc-4fdd-a4c8-7eb99dbc5a3a","added_by":"auto","created_at":"2025-12-31 16:18:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1530420,"visible":true,"origin":"","legend":"","description":"","filename":"daizuozhe.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6357683/v1_covered_f0568000-0bf1-4f73-911d-a2ec72a39304.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An enhanced Pied kingfisher optimizer for UAV path planning and engineering design problems","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":"Enhanced Pied Kingfisher Optimizer, Meta-heuristic algorithm, Swarm intelligence, UAV path planning, Engineering design problems","lastPublishedDoi":"10.21203/rs.3.rs-6357683/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6357683/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Pied Kingfisher Optimizer (PKO) is an advanced optimization algorithm. 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