Shape and sizing optimisation of space truss structures using a new cooperative coevolutionary-based algorithm

Etaati, B; Neshat, M; Dehkordi, AA; Pargoo, NS; El-Abd, M; Sadollah, A; Gandomi, AH

Shape and sizing optimisation of space truss structures using a new cooperative coevolutionary-based algorithm

Etaati, B Neshat, M

Dehkordi, AA Pargoo, NS El-Abd, M Sadollah, A Gandomi, AH

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Results in Engineering, 2024, 21, pp. 101859
Issue Date:: 2024-03-01

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Field	Value	Language
dc.contributor.author	Etaati, B
dc.contributor.author	Neshat, M https://orcid.org/0000-0002-9537-9513
dc.contributor.author	Dehkordi, AA
dc.contributor.author	Pargoo, NS
dc.contributor.author	El-Abd, M
dc.contributor.author	Sadollah, A
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2024-03-28T11:16:53Z
dc.date.available	2024-03-28T11:16:53Z
dc.date.issued	2024-03-01
dc.identifier.citation	Results in Engineering, 2024, 21, pp. 101859
dc.identifier.issn	2590-1230
dc.identifier.issn	2590-1230
dc.identifier.uri	http://hdl.handle.net/10453/177354
dc.description.abstract	Optimising the shape and size of large-scale truss frames is challenging because there is a nonlinear interaction between cross-sectional and nodal coordinate forces of structures. Meanwhile, combining the shape and bar size variables creates a multi-modal search space with dynamic constraints, making an expensive optimisation engineering problem. Besides, most of the real truss problems are large-scale, and optimisation algorithms are faced with the issue of scalability by increasing the size of the problem. This paper proposed a novel Cooperative Coevolutionary marine predators algorithm combined with a greedy search (CCMPA-GS) for truss optimisation on shape and sizing. The proposed algorithm used the divide-and-conquer technique to optimise the shape and size separately. Therefore, in each iteration, the CCMPA-GS focuses on shape optimisation initially and then switches to the size of bars and tries to find the best cooperative combination of the solutions in the current population using a context vector (CV). A greedy search is embedded in the following to fix the remaining violations from the structure's stress and displacement. This novel alternative optimisation strategy (CCMPA-GS) compared with 13 established genetic, evolutionary, swarm, and memetic meta-heuristic optimisation algorithms. The comparison is based on optimising two large-scale truss structures consisting of 260-bar and 314-bar configurations. Experimental results demonstrate that the proposed CCMPA-GS method consistently outperforms the other meta-heuristic methods, delivering optimal designs for the 314-bar and 260-bar truss structures that are superior by 52 % and 63.4 %, respectively. This signifies a substantial enhancement in optimisation performance, highlighting the potential of CCMPA-GS as a powerful alternative in the field of structural optimisation.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Results in Engineering
dc.relation.isbasedon	10.1016/j.rineng.2024.101859
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	40 Engineering
dc.title	Shape and sizing optimisation of space truss structures using a new cooperative coevolutionary-based algorithm
dc.type	Journal Article
utslib.citation.volume	21
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	2024-03-28T11:16:45Z
pubs.publication-status	Published
pubs.volume	21

Abstract:

Optimising the shape and size of large-scale truss frames is challenging because there is a nonlinear interaction between cross-sectional and nodal coordinate forces of structures. Meanwhile, combining the shape and bar size variables creates a multi-modal search space with dynamic constraints, making an expensive optimisation engineering problem. Besides, most of the real truss problems are large-scale, and optimisation algorithms are faced with the issue of scalability by increasing the size of the problem. This paper proposed a novel Cooperative Coevolutionary marine predators algorithm combined with a greedy search (CCMPA-GS) for truss optimisation on shape and sizing. The proposed algorithm used the divide-and-conquer technique to optimise the shape and size separately. Therefore, in each iteration, the CCMPA-GS focuses on shape optimisation initially and then switches to the size of bars and tries to find the best cooperative combination of the solutions in the current population using a context vector (CV). A greedy search is embedded in the following to fix the remaining violations from the structure's stress and displacement. This novel alternative optimisation strategy (CCMPA-GS) compared with 13 established genetic, evolutionary, swarm, and memetic meta-heuristic optimisation algorithms. The comparison is based on optimising two large-scale truss structures consisting of 260-bar and 314-bar configurations. Experimental results demonstrate that the proposed CCMPA-GS method consistently outperforms the other meta-heuristic methods, delivering optimal designs for the 314-bar and 260-bar truss structures that are superior by 52 % and 63.4 %, respectively. This signifies a substantial enhancement in optimisation performance, highlighting the potential of CCMPA-GS as a powerful alternative in the field of structural optimisation.

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http://hdl.handle.net/10453/177354