Optimization of retaining wall design using evolutionary algorithms

Gandomi, AH; Kashani, AR; Roke, DA; Mousavi, M

Optimization of retaining wall design using evolutionary algorithms

Gandomi, AH Kashani, AR Roke, DA Mousavi, M

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Publisher:: Springer
Publication Type:: Journal Article
Citation:: Structural and Multidisciplinary Optimization: computer-aided optimal design of stressed solids and multidisciplinary systems, 2017, 55, (3), pp. 809-825
Issue Date:: 2017-03-01

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Field	Value	Language
dc.contributor.author	Gandomi, AH
dc.contributor.author	Kashani, AR
dc.contributor.author	Roke, DA
dc.contributor.author	Mousavi, M
dc.date.accessioned	2022-08-25T03:02:52Z
dc.date.available	2022-08-25T03:02:52Z
dc.date.issued	2017-03-01
dc.identifier.citation	Structural and Multidisciplinary Optimization: computer-aided optimal design of stressed solids and multidisciplinary systems, 2017, 55, (3), pp. 809-825
dc.identifier.issn	1615-147X
dc.identifier.issn	1615-1488
dc.identifier.uri	http://hdl.handle.net/10453/160824
dc.description.abstract	This paper explores the performance of three evolutionary optimization methods, differential evolution (DE), evolutionary strategy (ES) and biogeography based optimization algorithm (BBO), for nonlinear constrained optimum design of a cantilever retaining wall. These algorithms are based on biological contests for survival and reproduction. The retaining wall optimization problem consists of two criteria, geotechnical stability and structural strength, while the final design minimizes an objective function. The objective function is defined in terms of both cost and weight. Constraints are applied using the penalty function method. The efficiency of the proposed method is examined by means of two numerical retaining wall design examples, one with a base shear key and one without a base shear key. The final designs are compared to the ones determined by genetic algorithms as classical metaheuristic optimization methods. The design results and convergence rate of the BBO algorithm show a significantly better performance than the other algorithms in both design cases.
dc.language	English
dc.publisher	Springer
dc.relation.ispartof	Structural and Multidisciplinary Optimization: computer-aided optimal design of stressed solids and multidisciplinary systems
dc.relation.isbasedon	10.1007/s00158-016-1521-3
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 09 Engineering
dc.subject.classification	Design Practice & Management
dc.title	Optimization of retaining wall design using evolutionary algorithms
dc.type	Journal Article
utslib.citation.volume	55
utslib.for	01 Mathematical Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-25T03:02:51Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	55
utslib.citation.issue	3

Abstract:

This paper explores the performance of three evolutionary optimization methods, differential evolution (DE), evolutionary strategy (ES) and biogeography based optimization algorithm (BBO), for nonlinear constrained optimum design of a cantilever retaining wall. These algorithms are based on biological contests for survival and reproduction. The retaining wall optimization problem consists of two criteria, geotechnical stability and structural strength, while the final design minimizes an objective function. The objective function is defined in terms of both cost and weight. Constraints are applied using the penalty function method. The efficiency of the proposed method is examined by means of two numerical retaining wall design examples, one with a base shear key and one without a base shear key. The final designs are compared to the ones determined by genetic algorithms as classical metaheuristic optimization methods. The design results and convergence rate of the BBO algorithm show a significantly better performance than the other algorithms in both design cases.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/160824