Publication:
A new metaheuristic algorithm: Shrimp and Goby association search algorithm and its application for damage identification in large-scale and complex structures

dc.contributor.author Thanh Sang-To, Hoang Le-Minh, Magd Abdel Wahab, Cuong-Le Thanh
dc.date.accessioned 2024-03-07T00:39:58Z
dc.date.available 2024-03-07T00:39:58Z
dc.date.issued 2022
dc.description.abstract "largescale global optimization problems. The performance of SGA is assessed using 13 benchmark high-dimensional functions, 10 classical benchmark functions, and several real-world engineering applications. For the first time, an efficient optimization approach for structural health monitoring (SHM) in truss-like structures is presented. The proposed approach is applied for damage identification of complex structures. A real structure, namely Canton Tower in Guangzhou, China, is served as an example for damage detection. Interestingly, this tower was the tallest structure in the world in 2009 with a height of 610 m. The great merit of this example is that it provides a real complex structure with a high-dimensional problem to assess the performance of SGA in the real world. The results show that SGA can deal with this problem effectively, at the same time, it operates better to escape from local optima with faster convergence rate than population-based algorithms."
dc.identifier.doi https://doi.org/10.1016/j.advengsoft.2022.103363
dc.identifier.uri http://repository.vlu.edu.vn:443/handle/123456789/12841
dc.language.iso en_US
dc.relation.ispartof Advances in Engineering Software
dc.relation.issn 0965-9978
dc.subject "SGA
dc.subject Damage detection
dc.subject High dimension
dc.subject Complex structures
dc.subject Optimization"
dc.title A new metaheuristic algorithm: Shrimp and Goby association search algorithm and its application for damage identification in large-scale and complex structures
dc.type Resource Types::text::journal::journal article
dspace.entity.type Publication
oairecerif.author.affiliation #PLACEHOLDER_PARENT_METADATA_VALUE#
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