Adaptive evolution strategies in structural optimization: Enhancing their computational performance with applications to large-scale structures
In this study the computational performance of adaptive evolution strategies (ESs) in large-scale structural optimization is mainly investigated to achieve the following objectives: (i) to present an ESs based solution algorithm for efficient optimum design of large structural systems consisting of...
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| Veröffentlicht in: | Computers & structures Jg. 86; H. 1; S. 119 - 132 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier Ltd
2008
Elsevier Science |
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| ISSN: | 0045-7949, 1879-2243 |
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| Abstract | In this study the computational performance of adaptive evolution strategies (ESs) in large-scale structural optimization is mainly investigated to achieve the following objectives: (i) to present an ESs based solution algorithm for efficient optimum design of large structural systems consisting of continuous, discrete and mixed design variables; (ii) to integrate new parameters and methodologies into adaptive ESs to improve the computational performance of the algorithm; and (iii) to assess successful self-adaptation models of ESs in continuous and discrete structural optimizations. A numerical example taken from the literature is studied in depth to verify the enhanced performance of the algorithm, as well as to scrutinize the role and significance of self-adaptation in ESs for a successfully implemented optimization process. Besides, the utility of the algorithm for practical structural engineering applications is demonstrated using a bridge design example. It is shown that adaptive ESs are reliable and powerful tools, and well-suited for optimum design of complex structural systems, including large-scale structural optimization. |
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| AbstractList | In this study the computational performance of adaptive evolution strategies (ESs) in large-scale structural optimization is mainly investigated to achieve the following objectives: (i) to present an ESs based solution algorithm for efficient optimum design of large structural systems consisting of continuous, discrete and mixed design variables; (ii) to integrate new parameters and methodologies into adaptive ESs to improve the computational performance of the algorithm; and (iii) to assess successful self-adaptation models of ESs in continuous and discrete structural optimizations. A numerical example taken from the literature is studied in depth to verify the enhanced performance of the algorithm, as well as to scrutinize the role and significance of self-adaptation in ESs for a successfully implemented optimization process. Besides, the utility of the algorithm for practical structural engineering applications is demonstrated using a bridge design example. It is shown that adaptive ESs are reliable and powerful tools, and well-suited for optimum design of complex structural systems, including large-scale structural optimization. |
| Author | Hasançebi, O. |
| Author_xml | – sequence: 1 givenname: O surname: HASANCEBI fullname: HASANCEBI, O organization: Middle East Technical University, Department of Civil Engineering, 06531 Ankara, Turkey |
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| Keywords | Adaptive penalty function Truss bridge design Size/shape optimum design of trusses Evolutionary algorithms Structural optimization Adaptive evolution strategies (ESs) Geometrical shape Evolutionary algorithm Complex system Large scale structure Modeling Geometrical model Penalty method Optimization Dimensioning Bridges Penalty function Truss structure Genetic algorithm Discrete programming Structural analysis |
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| SubjectTerms | Adaptive evolution strategies (ESs) Adaptive penalty function Computational techniques Evolutionary algorithms Exact sciences and technology Fundamental areas of phenomenology (including applications) Mathematical methods in physics Physics Size/shape optimum design of trusses Solid mechanics Static elasticity (thermoelasticity...) Structural and continuum mechanics Structural optimization Truss bridge design |
| Title | Adaptive evolution strategies in structural optimization: Enhancing their computational performance with applications to large-scale structures |
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