Enhanced Hybrid American Zebra Particle Swarm Optimization for Improving Energy Efficiency in Wireless Sensor Networks

Enhanced Hybrid American Zebra Particle Swarm Optimization for Improving Energy Efficiency in 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 Article Enhanced Hybrid American Zebra Particle Swarm Optimization for Improving Energy Efficiency in Wireless Sensor Networks Ahmed A. Mahmoud, Khaled Abd El Salam, Ahmed F. Ali, Rania Elgohary This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6834000/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 vast array of applications for Wireless Sensor Networks (WSNs) has made them a popular study topic in recent years. One major issue that WSNs must deal with is energy consumption, which directly affects the network's operational lifetime. To address this issue, clustering has emerged as an effective solution for minimizing energy usage; however, traditional algorithms like LEACH suffer from limited scalability and suboptimal cluster head selection. In clustering, SNs are constructed into multiple clusters while formed clusters are assigned a cluster head (CH) for centralizing communication and reduce energy consumption. This paper introduces a Hybrid American Zebra Particle Swarm Optimization (HAZPSO) algorithm, achieving up to 35% reduction in energy consumption compared to previous algorithms and increases the number of alive SNs by up to 40% compared to existing methods. HAZPSO algorithm combines the particle swarm optimization algorithm with American Zebra to achieve suitable CH. The algorithm works in two primary stages: the first stage selects the best CHs, and the second stage forms clusters using the Improved K-mean Clustering Algorithm (IKCA) based on the centroids identified in the first stage. Comparisons with eight other algorithms are used to assess the suggested HAZPSO algorithm's performance. The findings demonstrate that HAZPSO and IKCA considerably lowers energy usage while raising the nodes' leftover energy. In addition, compared to previous algorithms, it keeps a greater number of nodes alive, extending the network's operational lifetime. The results show that HAZPSO reduces energy consumption by 20% compared to GWO and 15% compared to MPA, while increasing the residual energy of the nodes. A future study might involve applying the suggested HAZPSO algorithm to real-time data operators and evaluating it in dynamic scenarios for the BS. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Physical sciences/Energy science and technology/Energy harvesting 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-6834000","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":478458757,"identity":"d0cd5b08-910f-4699-ae42-b94f27241a08","order_by":0,"name":"Ahmed A. 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One major issue that WSNs must deal with is energy consumption, which directly affects the network's operational lifetime. To address this issue, clustering has emerged as an effective solution for minimizing energy usage; however, traditional algorithms like LEACH suffer from limited scalability and suboptimal cluster head selection. In clustering, SNs are constructed into multiple clusters while formed clusters are assigned a cluster head (CH) for centralizing communication and reduce energy consumption. This paper introduces a Hybrid American Zebra Particle Swarm Optimization (HAZPSO) algorithm, achieving up to 35\\% reduction in energy consumption compared to previous algorithms and increases the number of alive SNs by up to 40\\% compared to existing methods. HAZPSO algorithm combines the particle swarm optimization algorithm with American Zebra to achieve suitable CH. The algorithm works in two primary stages: the first stage selects the best CHs, and the second stage forms clusters using the Improved K-mean Clustering Algorithm (IKCA) based on the centroids identified in the first stage. Comparisons with eight other algorithms are used to assess the suggested HAZPSO algorithm's performance. The findings demonstrate that HAZPSO and IKCA considerably lowers energy usage while raising the nodes' leftover energy. In addition, compared to previous algorithms, it keeps a greater number of nodes alive, extending the network's operational lifetime. The results show that HAZPSO reduces energy consumption by 20\\% compared to GWO and 15\\% compared to MPA, while increasing the residual energy of the nodes. A future study might involve applying the suggested HAZPSO algorithm to real-time data operators and evaluating it in dynamic scenarios for the BS.","manuscriptTitle":"Enhanced Hybrid American Zebra Particle Swarm Optimization for Improving Energy Efficiency in Wireless Sensor Networks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-02 12:35:38","doi":"10.21203/rs.3.rs-6834000/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"4ac2fbbb-f9de-4e8a-8722-3d9a31b9569b","owner":[],"postedDate":"July 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50798026,"name":"Physical sciences/Mathematics and computing/Computer science"},{"id":50798027,"name":"Physical sciences/Mathematics and computing/Information technology"},{"id":50798028,"name":"Physical sciences/Energy science and technology/Energy harvesting"}],"tags":[],"updatedAt":"2025-10-03T15:23:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-02 12:35:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6834000","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6834000","identity":"rs-6834000","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}