Stress-enhanced clonal selection algorithm for structural topology optimization.
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| Title: | Stress-enhanced clonal selection algorithm for structural topology optimization. |
|---|---|
| Authors: | Wu, C. Y.1, Ku, C. C.1 |
| Source: | International Journal for Numerical Methods in Engineering. 2/24/2012, Vol. 89 Issue 8, p957-974. 18p. |
| Abstract: | SUMMARY Recently, numerous modified versions of immune algorithms (IAs) have been adopted in both theoretical and practical applications. However, few have been proposed for solving structural topology optimization problems. In addition, the design connectivity handling and one-node connected hinge prevention, which are vital in the application of population-based methods with binary representation for structural topology optimization, have not been applied to IAs in the literature. A stress-enhanced clonal selection algorithm (SECSA) incorporating an IA with a dominance-based constraint-handling technique and a new stress-enhanced hypermutation operator is proposed to rectify those deficiencies. To demonstrate the high viability of the presented method, comparisons between the presented SECSA and genetic algorithm-based methods were made on minimum compliance and minimum weight benchmark structural topology design problems in two-dimensional, three-dimensional, and multiloading cases. In each case, SECSA was shown to be competitive in terms of convergence speed and solution quality. The main goal of this study is not only to further explore the capabilities of IAs, but also to show that an IA with appropriate enhancements can lead to the development of attractive computational tools for global search in structural topology optimization. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal for Numerical Methods in Engineering is the property of Wiley-Blackwell 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 70606761 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Stress-enhanced clonal selection algorithm for structural topology optimization. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wu%2C+C%2E+Y%2E%22">Wu, C. Y.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Ku%2C+C%2E+C%2E%22">Ku, C. C.</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+for+Numerical+Methods+in+Engineering%22">International Journal for Numerical Methods in Engineering</searchLink>. 2/24/2012, Vol. 89 Issue 8, p957-974. 18p. – Name: Abstract Label: Abstract Group: Ab Data: SUMMARY Recently, numerous modified versions of immune algorithms (IAs) have been adopted in both theoretical and practical applications. However, few have been proposed for solving structural topology optimization problems. In addition, the design connectivity handling and one-node connected hinge prevention, which are vital in the application of population-based methods with binary representation for structural topology optimization, have not been applied to IAs in the literature. A stress-enhanced clonal selection algorithm (SECSA) incorporating an IA with a dominance-based constraint-handling technique and a new stress-enhanced hypermutation operator is proposed to rectify those deficiencies. To demonstrate the high viability of the presented method, comparisons between the presented SECSA and genetic algorithm-based methods were made on minimum compliance and minimum weight benchmark structural topology design problems in two-dimensional, three-dimensional, and multiloading cases. In each case, SECSA was shown to be competitive in terms of convergence speed and solution quality. The main goal of this study is not only to further explore the capabilities of IAs, but also to show that an IA with appropriate enhancements can lead to the development of attractive computational tools for global search in structural topology optimization. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal for Numerical Methods in Engineering is the property of Wiley-Blackwell 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: 10.1002/nme.3270 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 957 Titles: – TitleFull: Stress-enhanced clonal selection algorithm for structural topology optimization. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wu, C. Y. – PersonEntity: Name: NameFull: Ku, C. C. IsPartOfRelationships: – BibEntity: Dates: – D: 24 M: 02 Text: 2/24/2012 Type: published Y: 2012 Identifiers: – Type: issn-print Value: 00295981 Numbering: – Type: volume Value: 89 – Type: issue Value: 8 Titles: – TitleFull: International Journal for Numerical Methods in Engineering Type: main |
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