A Multi-Strategy Enhanced Ivy Algorithm for Optimizing GANs to Improve Imbalanced Data Classification

A Multi-Strategy Enhanced Ivy Algorithm for Optimizing GANs to Improve Imbalanced Data Classification | 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 A Multi-Strategy Enhanced Ivy Algorithm for Optimizing GANs to Improve Imbalanced Data Classification Hanjie Xu, Jian Xiong, Jinyu Wu, Xianlai Zhou, Qiyu Chen, Ronghu Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7051032/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 Ivy Algorithm (IVYA), an emerging swarm intelligence optimization method, often faces challenges of slow convergence, local optima entrapment, and an imbalanced exploration-exploitation trade-off when applied to complex, high-dimensional problems. To address these deficiencies, this paper proposes an Enhanced Ivy Algorithm (E-IVYA). E-IVYA integrates three synergistic strategies: an elite-guided opposition-based learning mechanism to enhance population diversity, a stagnation response strategy to effectively escape local optima, and an adaptive movement strategy inspired by the Sine-Cosine Algorithm to dynamically balance global exploration and local exploitation. Experimental validation on the challenging IEEE CEC 2014 and 2017 benchmark suites demonstrates that E-IVYA’s performance significantly surpasses that of the original IVYA and various classic and advanced algorithms, including PSO, GWO, and LSHADE. Furthermore, E-IVYA shows excellent practical potential by optimizing the hyperparameters of Generative Adversarial Networks (GANs), which substantially improves classification performance on imbalanced datasets. These findings establish E-IVYA as a robust and superior optimizer, successfully overcoming the inherent limitations of the original algorithm. Physical sciences/Engineering Physical sciences/Mathematics and computing Swarm intelligence Ivy Algorithm Multi-strategy synergistic enhancement Hyperparameter optimization Imbalanced data classification 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. 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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-7051032","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":495231066,"identity":"242779d2-70b4-4694-b77f-62b05647475e","order_by":0,"name":"Hanjie 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