A Comparative State-of-the-Art Constrained Metaheuristics Framework for TRUSS Optimisation on Shape and Sizing

Etaati, B; Dehkordi, AA; Sadollah, A; El-Abd, M; Neshat, M

A Comparative State-of-the-Art Constrained Metaheuristics Framework for TRUSS Optimisation on Shape and Sizing

Etaati, B Dehkordi, AA Sadollah, A El-Abd, M Neshat, M

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Publisher:: Hindawi
Publication Type:: Journal Article
Citation:: Mathematical Problems in Engineering, 2022, 2022, pp. 1-13
Issue Date:: 2022-03-26

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Field	Value	Language
dc.contributor.author	Etaati, B
dc.contributor.author	Dehkordi, AA
dc.contributor.author	Sadollah, A
dc.contributor.author	El-Abd, M
dc.contributor.author	Neshat, M https://orcid.org/0000-0002-9537-9513
dc.contributor.editor	Leung, MF
dc.date.accessioned	2023-03-26T22:15:24Z
dc.date.available	2023-03-26T22:15:24Z
dc.date.issued	2022-03-26
dc.identifier.citation	Mathematical Problems in Engineering, 2022, 2022, pp. 1-13
dc.identifier.issn	1024-123X
dc.identifier.issn	1563-5147
dc.identifier.uri	http://hdl.handle.net/10453/168442
dc.description.abstract	<jats:p>In order to develop the dynamic effectiveness of the structures such as trusses, the application of optimisation methods plays a significant role in improving the shape and size of elements. However, conjoining two heterogeneous variables, nodal coordinates and cross-sectional elements, makes a challenging optimisation problem that is nonlinear, multimodal, large-scale with dynamic constraints. To handle these challenges, evolutionary and swarm optimisation algorithms can be robust and practical tools and show great potential to solve such complex problems. This paper proposed a comparative truss optimisation framework to solve two large-scale structures, including 314-bar and 260-bar trusses. The proposed framework consists of twelve state-of-the-art bio-inspired algorithms. The experimental results show that the marine predators algorithm (MPA) performed best compared with other algorithms in terms of convergence speed and the quality of the proposed designs of the trusses.</jats:p>
dc.language	en
dc.publisher	Hindawi
dc.relation.ispartof	Mathematical Problems in Engineering
dc.relation.isbasedon	10.1155/2022/6078986
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	01 Mathematical Sciences, 09 Engineering
dc.subject.classification	Numerical & Computational Mathematics
dc.title	A Comparative State-of-the-Art Constrained Metaheuristics Framework for TRUSS Optimisation on Shape and Sizing
dc.type	Journal Article
utslib.citation.volume	2022
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	open_access	*
dc.date.updated	2023-03-26T22:15:21Z
pubs.publication-status	Published
pubs.volume	2022

Abstract:

In order to develop the dynamic effectiveness of the structures such as trusses, the application of optimisation methods plays a significant role in improving the shape and size of elements. However, conjoining two heterogeneous variables, nodal coordinates and cross-sectional elements, makes a challenging optimisation problem that is nonlinear, multimodal, large-scale with dynamic constraints. To handle these challenges, evolutionary and swarm optimisation algorithms can be robust and practical tools and show great potential to solve such complex problems. This paper proposed a comparative truss optimisation framework to solve two large-scale structures, including 314-bar and 260-bar trusses. The proposed framework consists of twelve state-of-the-art bio-inspired algorithms. The experimental results show that the marine predators algorithm (MPA) performed best compared with other algorithms in terms of convergence speed and the quality of the proposed designs of the trusses.

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http://hdl.handle.net/10453/168442