SUNFLOWER OPTIMIZATION (SFO) ALGORITHM

Sunflower Optimization (Sfo) Algorithm For Nonlinear Unconstrained Optimization

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Sunflower Optimization (Sfo) Algorithm For Nonlinear Unconstrained Optimization

Copyright (c) 2018, Guilherme Ferreira Gomes
All rights reserved.

Please cite this algorithm as:

Gomes, G. F., da Cunha, S. S., & Ancelotti, A. C. A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates. Engineering with Computers, p. 1-8, 2018.
DOI: https://doi.org/10.1007/s00366-018-0620-8

Gomes, G. F., & de Almeida, F. A. (2020). Tuning metaheuristic algorithms using mixture design: Application of sunflower optimization for structural damage identification. Advances in Engineering Software, 149, 102877. https://doi.org/10.1016/j.advengsoft.2020.102877

Gomes, G. F., & Giovani, R. S. (2020). An efficient two-step damage identification method using sunflower optimization algorithm and mode shape curvature (MSDBI–SFO). Engineering with Computers. https://doi.org/10.1007/s00366-020-01128-2

Cita come

Guilherme Gomes (2026). SUNFLOWER OPTIMIZATION (SFO) ALGORITHM (https://it.mathworks.com/matlabcentral/fileexchange/69076-sunflower-optimization-sfo-algorithm), MATLAB Central File Exchange. Recuperato .

Gomes, Guilherme Ferreira, and Rafael Simões Giovani. “An Efficient Two-Step Damage Identification Method Using Sunflower Optimization Algorithm and Mode Shape Curvature (MSDBI–SFO).” Engineering with Computers, Springer Science and Business Media LLC, Aug. 2020, doi:10.1007/s00366-020-01128-2.

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Gomes, Guilherme Ferreira, and Fabricio Alves de Almeida. “Tuning Metaheuristic Algorithms Using Mixture Design: Application of Sunflower Optimization for Structural Damage Identification.” Advances in Engineering Software, vol. 149, Elsevier BV, Nov. 2020, p. 102877, doi:10.1016/j.advengsoft.2020.102877.

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Gomes, Guilherme Ferreira, et al. “A Sunflower Optimization (SFO) Algorithm Applied to Damage Identification on Laminated Composite Plates.” Engineering with Computers, vol. 35, no. 2, Springer Science and Business Media LLC, May 2018, pp. 619–26, doi:10.1007/s00366-018-0620-8.

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Informazioni generali

Compatibilità della release di MATLAB

  • Compatibile con qualsiasi release

Compatibilità della piattaforma

  • Windows
  • macOS
  • Linux
Versione Pubblicato Note della release Action
1.0.1

New published papers

1.0.0