Population-based optimization in structural engineering: a review

Kashani, AR; Camp, CV; Rostamian, M; Azizi, K; Gandomi, AH

Population-based optimization in structural engineering: a review

Kashani, AR Camp, CV Rostamian, M Azizi, K Gandomi, AH

Permalink

Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Artificial Intelligence Review, 2022, 55, (1), pp. 345-452
Issue Date:: 2022-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted versionAdobe PDF (2.39 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Kashani, AR
dc.contributor.author	Camp, CV
dc.contributor.author	Rostamian, M
dc.contributor.author	Azizi, K
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2023-03-27T05:02:25Z
dc.date.available	2023-03-27T05:02:25Z
dc.date.issued	2022-01-01
dc.identifier.citation	Artificial Intelligence Review, 2022, 55, (1), pp. 345-452
dc.identifier.issn	0269-2821
dc.identifier.issn	1573-7462
dc.identifier.uri	http://hdl.handle.net/10453/168584
dc.description.abstract	Structural engineering is focused on the safe and efficient design of infrastructure. Projects can range in size and complexity, many requiring massive amounts of materials and expensive construction and operational costs. Therefore, one of the primary objectives for structural engineers is a cost-effective design. Incorporating optimality criteria into the design procedure introduces additional complexities that result in problems that are nonlinear, nonconvex, and have a discontinuous solution space. Population-based optimization algorithms (known as metaheuristics) have been found to be very efficient approaches to these problems. Many researchers have developed and applied state-of-art metaheuristics to automate and optimize the design of real-world civil engineering problems. While there is a large body of published papers in this area, there are few comprehensive reviews that list, summarize, and categorize metaheuristic optimization in structural engineering. This paper provides an extensive survey of a wide range of metaheuristic techniques to structural engineering optimization problems. Also, information is provided on available structural engineering benchmark problems, the formulation of different objective functions, and the handling of various types of constraints. The performance of different optimization techniques is compared for many benchmark problems.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Artificial Intelligence Review
dc.relation.isbasedon	10.1007/s10462-021-10036-w
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. http://doi.org/10.1007/s10462-021-10036-w
dc.subject	0801 Artificial Intelligence and Image Processing, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Population-based optimization in structural engineering: a review
dc.type	Journal Article
utslib.citation.volume	55
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2023-03-27T05:02:24Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	55
utslib.citation.issue	1

Abstract:

Structural engineering is focused on the safe and efficient design of infrastructure. Projects can range in size and complexity, many requiring massive amounts of materials and expensive construction and operational costs. Therefore, one of the primary objectives for structural engineers is a cost-effective design. Incorporating optimality criteria into the design procedure introduces additional complexities that result in problems that are nonlinear, nonconvex, and have a discontinuous solution space. Population-based optimization algorithms (known as metaheuristics) have been found to be very efficient approaches to these problems. Many researchers have developed and applied state-of-art metaheuristics to automate and optimize the design of real-world civil engineering problems. While there is a large body of published papers in this area, there are few comprehensive reviews that list, summarize, and categorize metaheuristic optimization in structural engineering. This paper provides an extensive survey of a wide range of metaheuristic techniques to structural engineering optimization problems. Also, information is provided on available structural engineering benchmark problems, the formulation of different objective functions, and the handling of various types of constraints. The performance of different optimization techniques is compared for many benchmark problems.

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

http://hdl.handle.net/10453/168584