Title: Learning manta ray foraging optimisation based on external force for parameters identification of photovoltaic cell and module

Authors: Lin Zhang; Zuwei Huang; Donglin Zhu

Addresses: School of Software Engineering, JiangXi University of Science and Technology, Nanchang, 330000, China ' School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China ' School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China

Abstract: Photovoltaic systems, as a key area of research in the energy industry, face challenges from harsh environmental conditions that impact both power generation efficiency and service life. Therefore, constructing an accurate current-voltage model for solar cells is a complex issue. To address this, this paper proposes a Learning Manta Ray Algorithm based on External Force (EMRFO). The algorithm introduces two ways of random and opposition-based learning in the initialisation process to construct the initial population. Additionally, a self-adaptive flip factor is introduced to optimise performance across varying environments. Lastly, a gravity centre learning mechanism based on external force is proposed, which utilises both internal and external population information to enhance development and exploration capabilities. Experimental results demonstrate that EMRFO exhibits strong optimisation performance. In solar cell parameter identification, the parameters obtained using EMRFO improve model accuracy.

Keywords: solar cell; manta ray foraging optimisation; local opposition-based learning; somersault factor; centre of gravity learning; benchmark function; parameter identification.

DOI: 10.1504/IJAAC.2026.152109

International Journal of Automation and Control, 2026 Vol.20 No.2, pp.105 - 132

Received: 17 Jun 2023
Accepted: 28 May 2024
Published online: 09 Mar 2026 *

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