A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates
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The need for global damage detection methods that can be applied in complex structures has led to the development of methods that examine the structural dynamic behavior. The damage detection problem can be considered as a inverse problem with minimization of a objective function. For those reasons, a new nature-inspired optimization method based on sunflowers’ motion is introduced. The proposed sunflower optimization algorithm (SFO) technique is a population-based iterative heuristic global optimization algorithm for multi-modal problems. Compared to traditional algorithms, SFO employs terms as root velocity and pollination providing robustness. The new method is then applied in an inverse problem of structural damage detection in composite laminated plates.
KeywordsSunflower optimization Vibrations Laminated composite plate Inverse problem
The authors would like to acknowledge the financial support from the Brazilian agency CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico and CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior.
- 1.APDL AM (2010) Mechanical applications theory reference. ANSYS Release 13Google Scholar
- 6.Gomes GF (2016) Damage detection in laminated composite materials via optimization techniques and dynamic parameter. Master thesis, Federal University of ItajubáGoogle Scholar
- 10.Gomes GF, Mendez YAD, Alexandrino PDSL, da Cunha, SS, Ancelotti AC 2018. A review of vibration based inverse methods for damage detection and identification in mechanical structures using optimization algorithms and ANN. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-018-9273-4 Google Scholar
- 20.Mitchell M (1998) An introduction to genetic algorithms. MIT pressGoogle Scholar
- 26.Yang X-S (2012) Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation. Springer, Berlin, pp 240–249Google Scholar