Efficient Truss Design: A Hybrid Geometric Mean Optimizer for Better Performance
The emphasis on sustainable development is increasingly recognized as a global imperative, leading to significant transformations in methods and technologies within the construction industry. Specifically, the optimization of the mass of truss structures is aimed at enhancing sustainable constructio...
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2024/4216718 |
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| _version_ | 1832544758289924096 |
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| author | Vu Hong Son Pham Nghiep Trinh Nguyen Dang Van Nam Nguyen |
| author_facet | Vu Hong Son Pham Nghiep Trinh Nguyen Dang Van Nam Nguyen |
| author_sort | Vu Hong Son Pham |
| collection | DOAJ |
| description | The emphasis on sustainable development is increasingly recognized as a global imperative, leading to significant transformations in methods and technologies within the construction industry. Specifically, the optimization of the mass of truss structures is aimed at enhancing sustainable construction efforts. Such optimization is crucial for reducing dependence on natural resources and contributing to a decrease in CO2 emissions from the production and transport of construction materials. This study presents an innovative approach to the truss design challenge using a hybrid geometric mean optimizer (hGMO). The geometric mean optimizer (GMO) faces challenges in effectively exploring the solution space and avoiding local optima. To address these issues, GMO has been integrated with the variable neighborhood search (VNS) technique, thereby enhancing its exploration capabilities. The effectiveness of the hGMO model has been evaluated through four distinct truss design scenarios: 10-bar, 52-bar, 72-bar, and 200-bar structures. The results demonstrate that hGMO outperforms traditional methods, achieving optimal weights of 5060.915 lb, 1902.605 kg, 389.334 lb, and 25453.62 lb, respectively. These findings confirm that hGMO is a crucial tool in advancing sustainable construction practices by focusing on the strategic optimization of truss structure mass. |
| format | Article |
| id | doaj-art-768ce9f89d4f4bd286d53c7c61cdb828 |
| institution | Kabale University |
| issn | 1687-9732 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-768ce9f89d4f4bd286d53c7c61cdb8282025-02-03T09:55:37ZengWileyApplied Computational Intelligence and Soft Computing1687-97322024-01-01202410.1155/2024/4216718Efficient Truss Design: A Hybrid Geometric Mean Optimizer for Better PerformanceVu Hong Son Pham0Nghiep Trinh Nguyen Dang1Van Nam Nguyen2Faculty of Civil EngineeringFaculty of Civil EngineeringFaculty of Civil EngineeringThe emphasis on sustainable development is increasingly recognized as a global imperative, leading to significant transformations in methods and technologies within the construction industry. Specifically, the optimization of the mass of truss structures is aimed at enhancing sustainable construction efforts. Such optimization is crucial for reducing dependence on natural resources and contributing to a decrease in CO2 emissions from the production and transport of construction materials. This study presents an innovative approach to the truss design challenge using a hybrid geometric mean optimizer (hGMO). The geometric mean optimizer (GMO) faces challenges in effectively exploring the solution space and avoiding local optima. To address these issues, GMO has been integrated with the variable neighborhood search (VNS) technique, thereby enhancing its exploration capabilities. The effectiveness of the hGMO model has been evaluated through four distinct truss design scenarios: 10-bar, 52-bar, 72-bar, and 200-bar structures. The results demonstrate that hGMO outperforms traditional methods, achieving optimal weights of 5060.915 lb, 1902.605 kg, 389.334 lb, and 25453.62 lb, respectively. These findings confirm that hGMO is a crucial tool in advancing sustainable construction practices by focusing on the strategic optimization of truss structure mass.http://dx.doi.org/10.1155/2024/4216718 |
| spellingShingle | Vu Hong Son Pham Nghiep Trinh Nguyen Dang Van Nam Nguyen Efficient Truss Design: A Hybrid Geometric Mean Optimizer for Better Performance Applied Computational Intelligence and Soft Computing |
| title | Efficient Truss Design: A Hybrid Geometric Mean Optimizer for Better Performance |
| title_full | Efficient Truss Design: A Hybrid Geometric Mean Optimizer for Better Performance |
| title_fullStr | Efficient Truss Design: A Hybrid Geometric Mean Optimizer for Better Performance |
| title_full_unstemmed | Efficient Truss Design: A Hybrid Geometric Mean Optimizer for Better Performance |
| title_short | Efficient Truss Design: A Hybrid Geometric Mean Optimizer for Better Performance |
| title_sort | efficient truss design a hybrid geometric mean optimizer for better performance |
| url | http://dx.doi.org/10.1155/2024/4216718 |
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