Citation

Zamir, Samrina and Ishaque, Kashif and Javed, Saba and Alim, Muhammad Affan and Alam, Muhammad Mansoor and Mohd Su'ud, Mazliham (2025) Stagnation-Free PSO: A Random Reinitialization PSO (R²-PSO) Algorithm for Parameter Extraction of Solar Cells With Improved Speed, Accuracy, and Consistency. IEEE Access, 13. pp. 164787-164804. ISSN 2169-3536

Text Stagnation-Free PSO_ A Random Reinitialization PSO (R²-PSO) Algorithm for Parameter Extraction of Solar Cells With Improved Speed, Accuracy, and Consistency.pdf - Published Version Restricted to Repository staff only Download (4MB)

Abstract

Particle swarm optimization (PSO) often stagnates in photovoltaic (PV) parameter extraction due to premature convergence and velocity decay. This paper proposes Random Reinitialized PSO (R2 - PSO), which periodically reinitializes particle positions and velocities to restore diversity and sustain exploration without disrupting convergence. Performance is evaluated using the run-length distribution (RLD), a probabilistic benchmark that jointly characterizes convergence dynamics and steady-state success. In PV parameter extraction, R2 -PSO achieves RMSE = 10−9 , 100% success over 100 runs, and convergence in ≈1.05M function evaluations. Experimental validation on multi-crystalline (SM55), monocrystalline (S75), and thin-film (ST40) modules shows close agreement with manufacturer datasheets. Generalization capability is confirmed through the CEC-2022 single-objective benchmark suite, where R2 -PSO attains the lowest total rank and 1st overall position among five competitive PSO-based algorithms, securing best-mean performance on 8 out of 12 functions. Statistical comparisons across both application and benchmark domains indicate uniformly lower error metrics (SSE, MAE, MSE, AE, SE) relative to competing methods. Sensitivity analyses further demonstrate robustness, maintaining 100% success under variations in the VTR threshold, population size, maximum velocity, and epoch initialization frequency. These results establish R 2 -PSO as an accurate, reliable, and computationally efficient optimizer for PV parameter extraction, while highlighting RLD as a rigorous standard for evaluating stochastic search algorithms.

Item Type:	Article
Uncontrolled Keywords:	Particle swarm optimization
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 Sep 2025 08:40
Last Modified:	05 Oct 2025 16:19
URII:	http://shdl.mmu.edu.my/id/eprint/14617

Downloads

Edit (login required)