Orthogonal learning metaheuristics for structural optimization

Bakhshpoori, Taha; Abadi, Arash Asadi

doi:10.1007/s00521-023-08743-2

Neural Comput & Applic

2023

DOI: 10.1007/s00521-023-08743-2

|View full text |Cite

Orthogonal learning metaheuristics for structural optimization

Taha Bakhshpoori

Arash Asadi Abadi

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Achieving efficiency in truss structural design using opposition-based geometric mean optimizer

Pham,

Nguyen Dang,

Nguyen

2024

Soft Comput

View full text Add to dashboard Cite

Achieving efficiency in truss structural design using opposition-based geometric mean optimizer

Pham,

Nguyen Dang,

Nguyen

2024

Soft Comput

View full text Add to dashboard Cite

Efficient Truss Design: A Hybrid Geometric Mean Optimizer for Better Performance

Pham,

Nguyen Dang,

Nguyen

2024

Applied Computational Intelligence and Soft Computing

View full text Add to dashboard Cite

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.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Orthogonal learning metaheuristics for structural optimization

Cited by 2 publications

References 59 publications

Achieving efficiency in truss structural design using opposition-based geometric mean optimizer

Achieving efficiency in truss structural design using opposition-based geometric mean optimizer

Efficient Truss Design: A Hybrid Geometric Mean Optimizer for Better Performance

Contact Info

Product

Resources

About