Detecor algorithm: derived from the exploration of iterative methods

Detecor algorithm: derived from the exploration of iterative methods | 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 Detecor algorithm: derived from the exploration of iterative methods Jia Li, Weimin Zheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4806886/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 paper summarizes the pattern framework of some optimization algorithms, and proposes four optimization directions within this algorithmic pattern: search agent particles, search direction and perturbation, and search step size. Based on this pattern, the paper introduces an optimization algorithm called "Detector", and the name is just for convenient.In this algorithm, for the search direction and perturbation, as well as the search step size, it performs simple processing and only considers the optimization direction of the iterative search agent particles. For the current position of the search agent particles, a function is designed to evaluate this position. If the function value is greater than a threshold, the particle will not explore around that position, but move to another location.This optimization algorithm is designed without any natural inspirations, in order to prove the effectiveness of the proposed pattern framework. It also analyzes the limitations of this class of optimization algorithms.The algorithm is tested on the CEC2013, CEC2014, CEC2015, CEC2017, and CEC2022 test suites, and compared with 9 other algorithms, with a focus on the high-cost CEC2013 test suite. The algorithm performs well on most functions in the test suites. It uses a very small number of parameters and simple steps, without any natural inspirations. Physical sciences/Engineering Physical sciences/Mathematics and computing 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-4806886","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":342837318,"identity":"b0bd2a7e-7a0f-45d7-836d-869493d990ab","order_by":0,"name":"Jia Li","email":"","orcid":"","institution":"Shandong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Li","suffix":""},{"id":342837319,"identity":"2111940f-5739-42f4-b30c-c8a7ee0e023a","order_by":1,"name":"Weimin Zheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYDACCQglx8bMfODAhwoStBjzs7MlHpxxhgQtiTP7eYwP87YQoUN+dvOxh1/btiVuOMzz4QBvA4M8v9gB/FoY5xxLN5Ztu2284TDvhgOSOxgMZ85OwK+FWSLHTFpy221ZsBbDMwwJBrcJaGGTyP8G0sIIdNiDA4ltRGjhkchhk/y47bbizGYehgMHidEiIZFmJs3477YxPzObwcGGMxKE/SI/I/mZ5I8zt+XY+A8//vynwkaeX5qAFhBg5kGylbByEGD8QZy6UTAKRsEoGKkAABUJSJZArhk0AAAAAElFTkSuQmCC","orcid":"","institution":"Shandong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Weimin","middleName":"","lastName":"Zheng","suffix":""}],"badges":[],"createdAt":"2024-07-26 09:15:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4806886/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4806886/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68509407,"identity":"eaa89769-c9c5-4b72-812a-36fa3ecb77c7","added_by":"auto","created_at":"2024-11-08 05:24:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1112291,"visible":true,"origin":"","legend":"","description":"","filename":"Detecoralgorithmderivedfromtheexplorationofiterativemethods.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4806886/v1_covered_fe8051ac-70ce-42d6-b323-a5d21bbbf31e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Detecor algorithm: derived from the exploration of iterative methods","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":"","lastPublishedDoi":"10.21203/rs.3.rs-4806886/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4806886/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The paper summarizes the pattern framework of some optimization algorithms, and proposes four optimization directions within this algorithmic pattern: search agent particles, search direction and perturbation, and search step size. 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