Application of innovative SVM-PSO-GA algorithm to study vibrations of improved perovskite solar cells.
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| Title: | Application of innovative SVM-PSO-GA algorithm to study vibrations of improved perovskite solar cells. |
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| Authors: | Guo, Xiaojie1 (AUTHOR) Xiaojie_Guoo@outlook.com, Alharbi, Abdullah2 (AUTHOR), Alansari, Abdulrahman M.3 (AUTHOR) |
| Source: | Mechanics of Advanced Materials & Structures. 2025, Vol. 32 Issue 10, p2233-2250. 18p. |
| Subjects: | Machine learning, Hamilton's principle function, Solar cells, Elastic foundations, Support vector machines |
| Abstract: | This study investigates the vibrations of graphene oxide powders (GOPs) reinforced perovskite solar cells surrounded by an elastic foundation using both mathematical modeling and innovative machine learning algorithms. The incorporation of GOPs into the perovskite matrix enhances the mechanical properties and stability of the solar cells, which are crucial for their durability and efficiency. The analysis is conducted through the application of Hamilton's principle, providing a robust theoretical framework for deriving the governing equations of motion. An analytical method is employed to solve these equations, allowing for the accurate prediction of the vibrational behavior of the reinforced solar cells. The effects of various parameters, including the stiffness of the elastic foundation and the concentration of GOPs, are systematically examined. This study presents the application of an innovative Support Vector Machine (SVM)-Particle Swarm Optimization (PSO)-Genetic Algorithm (GA) to analyze the vibrations of GOPs reinforced perovskite solar cells surrounded by an elastic foundation using mathematical modeling datasets. The SVM-PSO-GA algorithm to enhance predictive accuracy. The integrated approach leverages the strengths of each method to model and predict the vibrational behavior of the reinforced solar cells. The results highlight the algorithm's effectiveness in capturing complex interactions and optimizing design parameters, providing valuable insights for improving the stability and performance of perovskite solar cells in practical applications. [ABSTRACT FROM AUTHOR] |
| Copyright of Mechanics of Advanced Materials & Structures is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 185132773 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Application of innovative SVM-PSO-GA algorithm to study vibrations of improved perovskite solar cells. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Guo%2C+Xiaojie%22">Guo, Xiaojie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> Xiaojie_Guoo@outlook.com</i><br /><searchLink fieldCode="AR" term="%22Alharbi%2C+Abdullah%22">Alharbi, Abdullah</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Alansari%2C+Abdulrahman+M%2E%22">Alansari, Abdulrahman M.</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Mechanics+of+Advanced+Materials+%26+Structures%22">Mechanics of Advanced Materials & Structures</searchLink>. 2025, Vol. 32 Issue 10, p2233-2250. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Hamilton's+principle+function%22">Hamilton's principle function</searchLink><br /><searchLink fieldCode="DE" term="%22Solar+cells%22">Solar cells</searchLink><br /><searchLink fieldCode="DE" term="%22Elastic+foundations%22">Elastic foundations</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study investigates the vibrations of graphene oxide powders (GOPs) reinforced perovskite solar cells surrounded by an elastic foundation using both mathematical modeling and innovative machine learning algorithms. The incorporation of GOPs into the perovskite matrix enhances the mechanical properties and stability of the solar cells, which are crucial for their durability and efficiency. The analysis is conducted through the application of Hamilton's principle, providing a robust theoretical framework for deriving the governing equations of motion. An analytical method is employed to solve these equations, allowing for the accurate prediction of the vibrational behavior of the reinforced solar cells. The effects of various parameters, including the stiffness of the elastic foundation and the concentration of GOPs, are systematically examined. This study presents the application of an innovative Support Vector Machine (SVM)-Particle Swarm Optimization (PSO)-Genetic Algorithm (GA) to analyze the vibrations of GOPs reinforced perovskite solar cells surrounded by an elastic foundation using mathematical modeling datasets. The SVM-PSO-GA algorithm to enhance predictive accuracy. The integrated approach leverages the strengths of each method to model and predict the vibrational behavior of the reinforced solar cells. The results highlight the algorithm's effectiveness in capturing complex interactions and optimizing design parameters, providing valuable insights for improving the stability and performance of perovskite solar cells in practical applications. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Mechanics of Advanced Materials & Structures is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/15376494.2024.2377412 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 2233 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Hamilton's principle function Type: general – SubjectFull: Solar cells Type: general – SubjectFull: Elastic foundations Type: general – SubjectFull: Support vector machines Type: general Titles: – TitleFull: Application of innovative SVM-PSO-GA algorithm to study vibrations of improved perovskite solar cells. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Guo, Xiaojie – PersonEntity: Name: NameFull: Alharbi, Abdullah – PersonEntity: Name: NameFull: Alansari, Abdulrahman M. IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 15376494 Numbering: – Type: volume Value: 32 – Type: issue Value: 10 Titles: – TitleFull: Mechanics of Advanced Materials & Structures Type: main |
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