Adaptive Pyrosome Optimization Algorithm (APOA): a novel algorithm for solving optimization and engineering problems

Adaptive Pyrosome Optimization Algorithm (APOA): a novel algorithm for solving optimization and engineering 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 Adaptive Pyrosome Optimization Algorithm (APOA): a novel algorithm for solving optimization and engineering problems Meifeng Shi, Cheng Tang, Yuan Chen, Hongwei Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6920468/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Inspired by the spores controlling during the life cycle of pyrosomes, this paper proposes a novel swarm intelligence-based meta-heuristic adaptive pyrosome optimization algorithm named APOA, which consists of four stages: Initialization, Information Interaction, Adaptive Decision-making, and Reaction. During the interaction, spores broadcast their information to each other. The decision-making stage employs a mechanism using random and adaptive strategies to enhance global exploration and local exploitation, respectively. Comprehensive tests on 23 test functions demonstrate the superior convergence and exceptional local optima avoidance of APOA. Extensive experimental results across CEC2014, CEC2017, CEC2020, and CEC2022 standard test suites reveal that APOA statistically outperforms 14 state-of-the-art metaheuristics in convergence speed, solution quality, and algorithmic stability. Furthermore, Empirical results on four types of engineering design problems further confirm that APOA is significantly superior to the leading competing algorithms in solving complex high-dimensional optimization challenges. Optimization Meta-heuristics Adaptive pyrosome optimization algorithm Adaptive decision-making mechanism Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Sep, 2025 Reviews received at journal 13 Jul, 2025 Reviews received at journal 11 Jul, 2025 Reviewers agreed at journal 11 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers invited by journal 09 Jul, 2025 Editor assigned by journal 25 Jun, 2025 Submission checks completed at journal 18 Jun, 2025 First submitted to journal 18 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. 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. 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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-6920468","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483513371,"identity":"4874956b-a442-4547-9ff5-10a1ff020e2a","order_by":0,"name":"Meifeng Shi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYDACCcYGhooKKIeHaC1nzgAZbMRrAeKzbaRokZ/d3Lrh4Dw72Q33GxgfvG1jkDcnpMXgzsG2Gwe3JRtvOMbAbDi3jcFwZwMhLRKJbbc/bjuQCNTCJs3bxpBgcICQw2YkAm2ZA9bC/psoLQw3QFoaILYwE6XFAKTlwLFk45nHEpsl55yTMNxA2GHpz24cqLGT7Tt8+OCHN2U28oQdBgXACAUiSDQRr2UUjIJRMApGAQ4AAE1ySIKNVhGYAAAAAElFTkSuQmCC","orcid":"","institution":"Chongqing University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Meifeng","middleName":"","lastName":"Shi","suffix":""},{"id":483513373,"identity":"e50d7dbd-1a56-4ac7-8a4f-317ad04546be","order_by":1,"name":"Cheng Tang","email":"","orcid":"","institution":"Chongqing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Tang","suffix":""},{"id":483513375,"identity":"a1f65b4e-3ef0-4acc-a29a-13335c537245","order_by":2,"name":"Yuan Chen","email":"","orcid":"","institution":"Chongqing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Chen","suffix":""},{"id":483513376,"identity":"7456824b-fe47-47fe-b1f3-78065dfbd178","order_by":3,"name":"Hongwei Zhang","email":"","orcid":"","institution":"Chongqing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Hongwei","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-06-18 07:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6920468/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6920468/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86478331,"identity":"0032704d-b56f-4dc1-aeae-2d5a839af0a2","added_by":"auto","created_at":"2025-07-11 07:11:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":35217111,"visible":true,"origin":"","legend":"","description":"","filename":"AdaptivePyrosomeOptimizationAlgorithmAPOAanovelalgorithmforsolvingoptimizationandengineeringproblems.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6920468/v1_covered_f5b6de20-1eb3-4c4a-a695-072b6d6492e1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adaptive Pyrosome Optimization Algorithm (APOA): a novel algorithm for solving optimization and engineering problems","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"cluster-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Cluster Computing](https://www.springer.com/journal/10586)","snPcode":"10586","submissionUrl":"https://submission.nature.com/new-submission/10586/3","title":"Cluster Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Optimization, Meta-heuristics, Adaptive pyrosome optimization algorithm, Adaptive decision-making mechanism","lastPublishedDoi":"10.21203/rs.3.rs-6920468/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6920468/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInspired by the spores controlling during the life cycle of pyrosomes, this paper proposes a novel swarm intelligence-based meta-heuristic adaptive pyrosome optimization algorithm named APOA, which consists of four stages: Initialization, Information Interaction, Adaptive Decision-making, and Reaction. 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