{"paper_id":"1bde8876-0fdb-4c3e-acae-97bc8ab039fa","body_text":"Improved Fractional-Order Darwinian Particle Swarm Optimization for Accurate Parameter Identification of Solar Photovoltaic Models | 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 Improved Fractional-Order Darwinian Particle Swarm Optimization for Accurate Parameter Identification of Solar Photovoltaic Models waleed ahmed, Samah abdeltawab This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9396568/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Parameter identification of photovoltaic (PV) models is essential for reliable performance prediction and efficient control of solar energy systems. However, the nonlinear and multimodal characteristics of PV equations make parameter estimation a challenging optimization problem. This paper proposes an Improved Fractional-Order Darwinian Particle Swarm Optimization (IFDPSO) algorithm for precise PV parameter extraction. The proposed method enhances conventional PSO through fractional-order velocity updating, which improves particle memory and increases search diversity. Also, a Darwinian selection mechanism removes stagnating swarms to maintain population diversity and prevent premature convergence. Adaptive adjustment of algorithm parameters further balances exploration and exploitation during the search process. The performance of IFDPSO is evaluated using the Single Diode, Double Diode, and Three Diode PV models of the RTC France solar cell. Results show that the proposed method achieves higher identification accuracy, faster convergence speed, and more stable solutions compared with conventional PSO and several recent metaheuristic optimization techniques. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Fractional Order Darwinian Particle Swarm Optimization Photovoltaic parameter estimation Single Diode Model Double Diode Model Three Diode Model Full Text Additional Declarations No competing interests reported. Supplementary Files S0130Run.xlsx S02Themfiles.zip Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 14 May, 2026 Reviewers agreed at journal 02 May, 2026 Reviewers agreed at journal 02 May, 2026 Reviews received at journal 22 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers invited by journal 20 Apr, 2026 Editor invited by journal 20 Apr, 2026 Editor assigned by journal 16 Apr, 2026 Submission checks completed at journal 16 Apr, 2026 First submitted to journal 12 Apr, 2026 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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