The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Comparison of evolutionary-based optimization algorithms for structural design optimization
Open accessAbstract
In this paper, a comparison of evolutionary-based optimization techniques for structural design optimization problems is presented. Furthermore, a hybrid optimization technique based on differential evolution algorithm is introduced for structural design optimization problems. In order to evaluate the proposed optimization approach a welded beam design problem taken from the literature is solved. The proposed approach is applied to a welded beam design problem and the optimal design of a vehicle component to illustrate how the present approach can be applied for solving structural design optimization problems. A comparative study of six population-based optimization algorithms for optimal design of the structures is presented. The volume reduction of the vehicle component is 28.4% using the proposed hybrid approach. The results show that the proposed approach gives better solutions compared to genetic algorithm, particle swarm, immune algorithm, artificial bee colony algorithm and differential evolution algorithm that are representative of the state-of-the-art in the evolutionary optimization literature.
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Cites
Storn, R., & Price, K. (1997) · Journal of Global Optimization · doi:10.1023/a:1008202821328 · Cited by 29,569
Cited by
Hassani, S., & Dackermann, U. (2023) · Sensors · doi:10.3390/s23063293 · Cited by 146
Metadata
- Authors
- Yildiz, A. R.
- Venue
- Engineering Applications of Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2013-01
- Volume
- 26
- Issue
- 1
- Pages
- 327-333
- ISSN
- 0952-1976 (ISSN-L)
- OpenAlex
- W2048882317
- Cited by
- 276
- References
- 1
- FWCI
- 25.33
- Type
- journal article
- Language
- en
- Open access
- green
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- Yildiz, A. R. (2013). Comparison of evolutionary-based optimization algorithms for structural design optimization. Engineering Applications of Artificial Intelligence. https://doi.org/10.1016/j.engappai.2012.05.014
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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