CLA-MRFO: A Synergistic Chaotic Lévy and Adaptive Restart Variant of Manta Ray Foraging Optimizer for Gene Feature Selection

CLA-MRFO: A Synergistic Chaotic Lévy and Adaptive Restart Variant of Manta Ray Foraging Optimizer for Gene Feature Selection | 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 CLA-MRFO: A Synergistic Chaotic Lévy and Adaptive Restart Variant of Manta Ray Foraging Optimizer for Gene Feature Selection Shamsuddeen Adamu, Hitham Alhussian, Said Jadid Abdulkadir, Ayed Alwadain, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7126869/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 24 You are reading this latest preprint version Abstract Swarm-based optimization algorithms often struggle to balance exploration and exploitation in complex, high-dimensional search spaces. While Manta Ray Foraging Optimization (MRFO) has demonstrated competitive performance in recent studies, its effectiveness remains constrained by rigid parameter settings and phase-specific stagnation. This paper introduces the synergistic Chaotic Lévy and Adaptive Restart variant of MRFO (CLA-MRFO), which integrates chaotic dynamics, Lévy-driven step-size modulation, and phase-aware memory into a cohesive framework. Unlike in-cremental hybrids, CLA-MRFO enables dynamic interaction among its components: chaotic maps adaptively regulate Lévy flight (LF) behavior to improve global exploration; memory mechanisms preserve diverse elite solutions across search phases; and an entropy-informed restart strategy injects diversity when stagnation is detected, without disrupting convergence stability.The proposed method is evaluated on both the CEC’17 benchmark suite and a real-world, high-dimensional gene expression dataset for leukemia classification. CLA-MRFO outperforms eight recent metaheuristic algorithms, including MRFO, PSO, and GBO variants, across the majority of benchmark functions. In the context of gene feature selection (FS), CLA-MRFO successfully identifies compact, discriminative gene subsets that yield robust classification performance across six machine learning models. Using a 5-fold nested cross-validation protocol, the selected features achieved average F1-scores exceeding 0.95. Statistical analyses confirm that these improvements are both stable and significant.These results position CLA-MRFO as a robust, generalizable, and interpretable optimizer for both synthetic benchmarks and real-world biomedical FS tasks. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Physical sciences/Mathematics and computing Metaheuristic Optimization Manta Ray Foraging Optimization Feature Selection L´evy Flight and Chaotic Maps Gene Expression Classification Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 25 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 08 Aug, 2025 Reviews received at journal 05 Aug, 2025 Reviewers agreed at journal 05 Aug, 2025 Reviewers agreed at journal 05 Aug, 2025 Reviewers agreed at journal 05 Aug, 2025 Reviews received at journal 04 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviews received at journal 04 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviews received at journal 04 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers invited by journal 04 Aug, 2025 Editor assigned by journal 04 Aug, 2025 Editor invited by journal 17 Jul, 2025 Submission checks completed at journal 16 Jul, 2025 First submitted to journal 16 Jul, 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. 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-7126869","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":496067664,"identity":"3157e1c7-68ab-4e3f-99f8-bac2fa89a4d0","order_by":0,"name":"Shamsuddeen Adamu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYDACCTBpw8PG3sDATIqWNDl+ngOkaTlsLDkjgUgt8rObn2782cacuOHmG8PPBRU2DPzt3Ql4tRjcOWZ2m7eNLXHD7Rxj6Rln0hgkzpzdgF+LRILZbcY2HpAWA2netsNAkVz8WuRnpH+7+bNNAuiwM8a/idLCcCPH7AZvmwHQ+zxmxNlicCOn7DbPuQRgIKeVWfOcSeMh6Begw7bd/FH2HxiVhzff5qmwkeNv7yXgMATgMACRPMQqBwH2B6SoHgWjYBSMghEEAMoXSAavGdZ5AAAAAElFTkSuQmCC","orcid":"","institution":"IAIICT, Ahmadu Bello University","correspondingAuthor":true,"prefix":"","firstName":"Shamsuddeen","middleName":"","lastName":"Adamu","suffix":""},{"id":496067665,"identity":"76e7959d-d32f-4676-8cf3-c6a774690df6","order_by":1,"name":"Hitham Alhussian","email":"","orcid":"","institution":"Universiti Teknologi PETRONAS","correspondingAuthor":false,"prefix":"","firstName":"Hitham","middleName":"","lastName":"Alhussian","suffix":""},{"id":496067666,"identity":"5c62fb88-e092-4b37-86fe-b99580ab3108","order_by":2,"name":"Said Jadid Abdulkadir","email":"","orcid":"","institution":"Universiti Teknologi PETRONAS","correspondingAuthor":false,"prefix":"","firstName":"Said","middleName":"Jadid","lastName":"Abdulkadir","suffix":""},{"id":496067667,"identity":"69200686-afc3-46e3-b375-9af539bde9d4","order_by":3,"name":"Ayed Alwadain","email":"","orcid":"","institution":"Community College, King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Ayed","middleName":"","lastName":"Alwadain","suffix":""},{"id":496067668,"identity":"4bc96f76-3a33-45e8-8ed2-59cc24ad69c3","order_by":4,"name":"Sallam O.F. Khairy","email":"","orcid":"","institution":"University of Nizwa","correspondingAuthor":false,"prefix":"","firstName":"Sallam","middleName":"O.F.","lastName":"Khairy","suffix":""},{"id":496067669,"identity":"edee4beb-3870-46c5-923a-bed15dc0ab8d","order_by":5,"name":"Hussaini Mamman","email":"","orcid":"","institution":"Universiti Teknologi PETRONAS","correspondingAuthor":false,"prefix":"","firstName":"Hussaini","middleName":"","lastName":"Mamman","suffix":""},{"id":496067670,"identity":"fae6c79a-bdf9-4aca-af4b-475347520a43","order_by":6,"name":"Ismail Said Nasir Almuniri","email":"","orcid":"","institution":"University of Nizwa","correspondingAuthor":false,"prefix":"","firstName":"Ismail","middleName":"Said Nasir","lastName":"Almuniri","suffix":""},{"id":496067671,"identity":"30226d5d-e494-4da9-ac34-4aa8af412097","order_by":7,"name":"Al Abri Al Waleed Sulaiman Zahir","email":"","orcid":"","institution":"University of Nizwa","correspondingAuthor":false,"prefix":"","firstName":"Al","middleName":"Abri Al Waleed Sulaiman","lastName":"Zahir","suffix":""},{"id":496067672,"identity":"5d0a9b0c-dc3e-44b8-9b2b-9537e06813b3","order_by":8,"name":"Zaid Fawaz Jarallah","email":"","orcid":"","institution":"University of Mosul","correspondingAuthor":false,"prefix":"","firstName":"Zaid","middleName":"Fawaz","lastName":"Jarallah","suffix":""},{"id":496067673,"identity":"576ade80-e846-46e8-b382-50ddc7be6275","order_by":9,"name":"Hamood Saif Hamood Hamood Al Fahdi","email":"","orcid":"","institution":"Aljabel Alakhder Sultanate of Oman","correspondingAuthor":false,"prefix":"","firstName":"Hamood","middleName":"Saif Hamood Hamood Al","lastName":"Fahdi","suffix":""}],"badges":[],"createdAt":"2025-07-15 06:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7126869/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7126869/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-25766-y","type":"published","date":"2025-11-25T15:58:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":97178463,"identity":"20d84ede-b3a0-49a1-af48-136f360152cb","added_by":"auto","created_at":"2025-12-01 16:10:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1365341,"visible":true,"origin":"","legend":"","description":"","filename":"14MainArticle12July25ProgressinBiomedicalEngineering.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7126869/v1_covered_ef738dd1-70c8-46b9-a5ff-3cdfb525e27d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CLA-MRFO: A Synergistic Chaotic Lévy and Adaptive Restart Variant of Manta Ray Foraging Optimizer for Gene Feature Selection","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Metaheuristic Optimization, Manta Ray Foraging Optimization, Feature Selection, L´evy Flight and Chaotic Maps, Gene Expression Classification","lastPublishedDoi":"10.21203/rs.3.rs-7126869/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7126869/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Swarm-based optimization algorithms often struggle to balance exploration and exploitation in complex, high-dimensional search spaces. While Manta Ray Foraging Optimization (MRFO) has demonstrated competitive performance in recent studies, its effectiveness remains constrained by rigid parameter settings and phase-specific stagnation. This paper introduces the synergistic Chaotic Lévy and Adaptive Restart variant of MRFO (CLA-MRFO), which integrates chaotic dynamics, Lévy-driven step-size modulation, and phase-aware memory into a cohesive framework. Unlike in-cremental hybrids, CLA-MRFO enables dynamic interaction among its components: chaotic maps adaptively regulate Lévy flight (LF) behavior to improve global exploration; memory mechanisms preserve diverse elite solutions across search phases; and an entropy-informed restart strategy injects diversity when stagnation is detected, without disrupting convergence stability.The proposed method is evaluated on both the CEC’17 benchmark suite and a real-world, high-dimensional gene expression dataset for leukemia classification. CLA-MRFO outperforms eight recent metaheuristic algorithms, including MRFO, PSO, and GBO variants, across the majority of benchmark functions. In the context of gene feature selection (FS), CLA-MRFO successfully identifies compact, discriminative gene subsets that yield robust classification performance across six machine learning models. Using a 5-fold nested cross-validation protocol, the selected features achieved average F1-scores exceeding 0.95. Statistical analyses confirm that these improvements are both stable and significant.These results position CLA-MRFO as a robust, generalizable, and interpretable optimizer for both synthetic benchmarks and real-world biomedical FS tasks.","manuscriptTitle":"CLA-MRFO: A Synergistic Chaotic Lévy and Adaptive Restart Variant of Manta Ray Foraging Optimizer for Gene Feature Selection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-06 02:46:55","doi":"10.21203/rs.3.rs-7126869/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-08T11:57:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-05T14:02:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299697213954867202091431703719493336372","date":"2025-08-05T08:49:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"206920550557690192807107438789470758583","date":"2025-08-05T04:34:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"55732203458673264618102619358435952016","date":"2025-08-05T04:29:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-05T01:15:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96857165219644559700402828215784818424","date":"2025-08-05T00:11:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219651087146319705187351744756251614065","date":"2025-08-05T00:00:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1306856300304455791055689892304209395","date":"2025-08-04T19:52:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198662387367641134481973183794588468241","date":"2025-08-04T18:28:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256366781363259284662351518808236993602","date":"2025-08-04T16:48:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-04T16:39:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23782254046125429419705479561484773565","date":"2025-08-04T15:57:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-04T15:57:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281558697755043473197711574972069071458","date":"2025-08-04T15:35:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96266669563729229990007646197506118481","date":"2025-08-04T15:17:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51181695240816431220877651865461026688","date":"2025-08-04T15:17:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"212473462044569704824844922072927972353","date":"2025-08-04T14:39:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245830024409214726128958683751428959517","date":"2025-08-04T14:23:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-04T14:18:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-04T14:10:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-17T06:47:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-16T06:51:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-16T06:49:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"25145e0f-d25a-4632-ac72-5b190d47c78d","owner":[],"postedDate":"August 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":52687642,"name":"Biological sciences/Cancer"},{"id":52687643,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":52687644,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-12-01T16:03:11+00:00","versionOfRecord":{"articleIdentity":"rs-7126869","link":"https://doi.org/10.1038/s41598-025-25766-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-25 15:58:13","publishedOnDateReadable":"November 25th, 2025"},"versionCreatedAt":"2025-08-06 02:46:55","video":"","vorDoi":"10.1038/s41598-025-25766-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-25766-y","workflowStages":[]},"version":"v1","identity":"rs-7126869","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7126869","identity":"rs-7126869","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}