Adaptive Hybrid Sperm Swarm Optimization and Genetic Algorithm (aHSSOGA) in clustering of Wireless Sensor Networks

Adaptive Hybrid Sperm Swarm Optimization and Genetic Algorithm (aHSSOGA) in clustering of Wireless Sensor Networks | 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 Hybrid Sperm Swarm Optimization and Genetic Algorithm (aHSSOGA) in clustering of Wireless Sensor Networks Bryan Raj, Maidul Hasan Masum, Ismail Ahmedy, Mohd Yamani Idna Idris This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8886945/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 Wireless Sensor Networks face significant energy constraints that directly impact network lifetime and data collection efficiency. While Low-Energy Adaptive Clustering Hierarchy (LEACH) has been foundational since the early 2000s, persistent challenges including isolated nodes and energy hotspots limit its effectiveness. This work proposes adaptive Hybrid Sperm Swarm Optimization and Genetic Algorithm (aHSSOGA), a metaheuristic approach that combines adaptive parameter tuning with refined objective functions to optimize cluster head selection. Unlike conventional methods, aHSSOGA dynamically adjusts crossover and mutation probabilities based on operator performance and incorporates velocity dampening to balance exploration and exploitation. The five refined objective functions specifically target isolated node and hotspot mitigation. Implemented with an enhanced LEACH re-clustering mechanism that reduces overhead, aHSSOGA demonstrates substantial improvements over LEACH, HSAPSO, HFAPSO, HGWOSFO, and standard HSSOGA across six key performance metrics: average residual energy (5.55% to 9.12% improvement), network lifetime (7.18% to 19.26% improvement), re-clustering frequency (93.5% reduction), total data delivery (11.98% to 32.36% improvement), network throughput, and end-to-end delay. These results confirm that adaptive hybrid metaheuristics with refined objectives provide a practical and effective solution for extending WSN operational lifespan. Wireless Sensor Networks Cluster Head Selection Energy-Efficient Clustering Network Lifetime 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-8886945","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592094223,"identity":"428941e8-fd97-40c7-b9ca-9dbfebeb2352","order_by":0,"name":"Bryan Raj","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACAyjNzCZ/+ACIwdhAtBY+CbYE0rQwyEnwGBCnxVz6jOHnwj127GzSPd+kbjDYyG44wJ0mgU+LZV+OsfSMZ8nMbDJnt0nnMKQZbzjAuw2vFoMzPAbSPAeYmdkYckFaDicSo8X4N8+BeqCWnGdALf+J0mIGtOUwM5tEDhtQywHCWix72MqseQ4cZ2bjOWZsnWOQbDzzMO9mC3xazHmYN9/mOVCdLN/e/PB2ToWdbN/x3o038GlhYOAAR0cy1J1AzMzAgtdhDAzsD0CkHbIQ8wf8WkbBKBgFo2CEAQAxb0N9GCxORgAAAABJRU5ErkJggg==","orcid":"","institution":"Universiti Malaya","correspondingAuthor":true,"prefix":"","firstName":"Bryan","middleName":"","lastName":"Raj","suffix":""},{"id":592094224,"identity":"f377d094-8d31-4ac2-ba9a-1625ba28e726","order_by":1,"name":"Maidul Hasan Masum","email":"","orcid":"","institution":"Universiti Malaya","correspondingAuthor":false,"prefix":"","firstName":"Maidul","middleName":"Hasan","lastName":"Masum","suffix":""},{"id":592094225,"identity":"8b44d7e6-23c9-442c-bc41-b192c229d886","order_by":2,"name":"Ismail Ahmedy","email":"","orcid":"","institution":"Universiti Malaya","correspondingAuthor":false,"prefix":"","firstName":"Ismail","middleName":"","lastName":"Ahmedy","suffix":""},{"id":592094226,"identity":"dcfe287b-d286-4ff5-b55a-219f0f233065","order_by":3,"name":"Mohd Yamani Idna Idris","email":"","orcid":"","institution":"Universiti Malaya","correspondingAuthor":false,"prefix":"","firstName":"Mohd","middleName":"Yamani Idna","lastName":"Idris","suffix":""}],"badges":[],"createdAt":"2026-02-15 15:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8886945/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8886945/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103402037,"identity":"cfa1834c-0fc7-4ba0-a460-8b30e384cd3b","added_by":"auto","created_at":"2026-02-25 09:28:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":957669,"visible":true,"origin":"","legend":"","description":"","filename":"aHSSOGAinclusteringofWirelessSensorNetworks.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8886945/v1_covered_22358b3d-3917-4fe2-a90f-63173932e687.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adaptive Hybrid Sperm Swarm Optimization and Genetic Algorithm (aHSSOGA) in clustering of Wireless Sensor Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Wireless Sensor Networks, Cluster Head Selection, Energy-Efficient Clustering, Network Lifetime","lastPublishedDoi":"10.21203/rs.3.rs-8886945/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8886945/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWireless Sensor Networks face significant energy constraints that directly impact network lifetime and data collection efficiency. While Low-Energy Adaptive Clustering Hierarchy (LEACH) has been foundational since the early 2000s, persistent challenges including isolated nodes and energy hotspots limit its effectiveness. This work proposes adaptive Hybrid Sperm Swarm Optimization and Genetic Algorithm (aHSSOGA), a metaheuristic approach that combines adaptive parameter tuning with refined objective functions to optimize cluster head selection. Unlike conventional methods, aHSSOGA dynamically adjusts crossover and mutation probabilities based on operator performance and incorporates velocity dampening to balance exploration and exploitation. The five refined objective functions specifically target isolated node and hotspot mitigation. 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