A novel reflective intelligence optimizer with machine learning (RIO-ML) for parameter estimation of photovoltaic models.

Ahmed Bayoumi, Mahana M Elbana, A A Nasef, M E Ali

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 authors.

Ahmed BayoumiPhysics and Engineering Mathematics Department, Faculty of Engineering, Kafrelshiekh University, Kafrelshiekh, 33516, Egypt.ORCID http://orcid.org/0000-0002-1067-5445

Mahana M ElbanaPhysics and Engineering Mathematics Department, Faculty of Engineering, Kafrelshiekh University, Kafrelshiekh, 33516, Egypt. mehna.mohsen@eng.kfs.edu.eg.ORCID http://orcid.org/0009-0005-1946-8150

A A NasefPhysics and Engineering Mathematics Department, Faculty of Engineering, Kafrelshiekh University, Kafrelshiekh, 33516, Egypt.ORCID http://orcid.org/0000-0003-4705-6098

M E AliPhysics and Engineering Mathematics Department, Faculty of Engineering, Kafrelshiekh University, Kafrelshiekh, 33516, Egypt.ORCID http://orcid.org/0000-0003-0087-6144

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper presents a new Reflective Intelligence Optimizer with Machine Learning (RIO-ML) approach to estimate the parameters of solar photovoltaic (PV) equivalent circuit models, which are highly nonlinear and multimodal, and hence require efficient handling by conventional optimizers. RIO-ML combines three major components: a multi-leader social learning algorithm with personal-best reflective memory, machine learning-driven adaptive control of important parameters using Multi-Layer Perceptron models, and progressive Gaussian refinement with reflective boundary treatment for improved convergence and robustness. The performance of RIO-ML is tested on the standard RTC France solar cell model with three different model settings: Single Diode Model (SDM) with 5 parameters, Double Diode Model (DDM) with 7 parameters, and Triple Diode Model (TDM) with 9 parameters, for 30 independent runs for each scenario. RIO-ML obtains the minimum RMSE of 8.739710 × 10

Indexed as

Adaptive controlMachine learningMetaheuristic optimizationParameter estimationPhotovoltaicReflective intelligence

Identifiers

PMID42426073

DOI10.1038/s41598-026-60435-8

PMCPMC13351024

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