Sunflower optimization algorithm for multi-strategy-assist parameter identification of solar cell models

A novel optimization method, namely, the elite opposition learning and polynomial steps-based sunflower optimization (EOPSFO) algorithm, has been proposed to solve engineering problems. To speed up the convergence, the elite opposition-based learning and polynomial steps strategy is applied to autom...

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Bibliographic Details
Published in:AIP advances 2023-05, Vol.13 (5), p.055026-055026-9
Main Authors: Lv, Liye, Yuan, Yongliang
Format: Article
Language:English
Subjects:
Algorithms
Iterative methods
Mathematical models
Optimization
Parameter identification
Photovoltaic cells
Polynomials
Solar cells
Strategy
Sunflowers
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Summary:A novel optimization method, namely, the elite opposition learning and polynomial steps-based sunflower optimization (EOPSFO) algorithm, has been proposed to solve engineering problems. To speed up the convergence, the elite opposition-based learning and polynomial steps strategy is applied to automatically determine the search step adapted in each iteration. To verify the performance of EOPSFO, the feasibility of EOPSFO is first verified using various benchmarking and some standard optimization problems. In addition, EOPSFO is used to determine the parameters of the single diode model and double diode model. Results show that EOPSFO can be regarded as a competitive algorithm in optimization problems.
ISSN:2158-3226
2158-3226
DOI:10.1063/5.0149442