A Hybrid Approach for Optimal Cluster Head Selection in VANETs with Various Topologies Using Fuzzy Logic, Moth Flame Optimization, and Machine Learning

A Hybrid Approach for Optimal Cluster Head Selection in VANETs with Various Topologies Using Fuzzy Logic, Moth Flame Optimization, and Machine Learning | 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 A Hybrid Approach for Optimal Cluster Head Selection in VANETs with Various Topologies Using Fuzzy Logic, Moth Flame Optimization, and Machine Learning Mosayeb Soleymaninasab, Ehsan Kharati, Sara Taghipour, Mona sharifnezhad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7852002/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 Nowadays, the Vehicular Ad hoc Network (VANET) technology is used to improve the quality of the transportation systems and road safety between Vehicles (Vs). The main routing challenges in VANETs include their dynamic and unstable structure, energy limitations of the Vs, and the use of intermediate Vs. Clustering is used to balance the overhead, increase lifetime, and enhance data collection in VANETs. Finding the optimal Cluster Head (CH) is an NP-hard problem, and heuristic and metaheuristic methods are often employed to solve it. In this paper, we propose a method for routing and optimal CH selection among all. In each cycle and across all VANETs with different topologies, various Vs features are first collected, and then using a heuristic method, the Fuzzy Inference System (FIS), the Optimization Fitness Function ( OFF ) value of all Vs is calculated to determine the optimal CHs. Additionally, the metaheuristic Moth Flame Optimization (MFO) algorithm is used to tune and set the coefficients and rules of the FIS. Finally, to train and test VANET behavior patterns across various topologies, Decision Trees (DTs) based on the Random Forest (RF) ensemble Machine Learning (ML) method are utilized. Simulation results show that the proposed method outperforms clustering-based routing protocols such as LEACH, AODV, DSRC, CBRP, and GPSR in VANETs in terms of the number of alive and dead Vs, average network lifetime, routing overhead, end-to-end delay, throughput, packet delivery rate, and execution time. VANETs Clustering CH FIS MFO RF ML OFF DT 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. 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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-7852002","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":545613193,"identity":"ae93a85c-cf1f-440f-bbab-6e912a0b6d63","order_by":0,"name":"Mosayeb Soleymaninasab","email":"","orcid":"","institution":"Islamic Azad University, Arak","correspondingAuthor":false,"prefix":"","firstName":"Mosayeb","middleName":"","lastName":"Soleymaninasab","suffix":""},{"id":545613194,"identity":"e0bd766a-561c-49e5-a386-bbb65cbc6642","order_by":1,"name":"Ehsan 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DT","lastPublishedDoi":"10.21203/rs.3.rs-7852002/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7852002/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNowadays, the Vehicular Ad hoc Network (VANET) technology is used to improve the quality of the transportation systems and road safety between Vehicles (Vs). The main routing challenges in VANETs include their dynamic and unstable structure, energy limitations of the Vs, and the use of intermediate Vs. Clustering is used to balance the overhead, increase lifetime, and enhance data collection in VANETs. Finding the optimal Cluster Head (CH) is an NP-hard problem, and heuristic and metaheuristic methods are often employed to solve it. In this paper, we propose a method for routing and optimal CH selection among all. In each cycle and across all VANETs with different topologies, various Vs features are first collected, and then using a heuristic method, the Fuzzy Inference System (FIS), the Optimization Fitness Function (\u003cem\u003eOFF\u003c/em\u003e) value of all Vs is calculated to determine the optimal CHs. Additionally, the metaheuristic Moth Flame Optimization (MFO) algorithm is used to tune and set the coefficients and rules of the FIS. Finally, to train and test VANET behavior patterns across various topologies, Decision Trees (DTs) based on the Random Forest (RF) ensemble Machine Learning (ML) method are utilized. Simulation results show that the proposed method outperforms clustering-based routing protocols such as LEACH, AODV, DSRC, CBRP, and GPSR in VANETs in terms of the number of alive and dead Vs, average network lifetime, routing overhead, end-to-end delay, throughput, packet delivery rate, and execution time.\u003c/p\u003e","manuscriptTitle":"A Hybrid Approach for Optimal Cluster Head Selection in VANETs with Various Topologies Using Fuzzy Logic, Moth Flame Optimization, and Machine Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 04:21:16","doi":"10.21203/rs.3.rs-7852002/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":"63bf10d4-af7c-4e0b-8511-30993e430b19","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-10T19:38:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-17 04:21:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7852002","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7852002","identity":"rs-7852002","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}