Constrained optimization: Artificial Bee Colony algorithm
Artificial Bee Colony algorithm supported by Deb's rules to handle constraints.
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This is implementation of Artificial Bee Colony algorithm that can solve constrained optimization problems. To handle constraints the Deb's rules have been used to compare the actual and new solutions. The implementation of objective function that have to be optimized, has to return two values: main objective function (
) and violation function (
). The algorithm maximized with subject to 
.
) and violation function (). The algorithm maximized with subject to . The exmaple implementation solve the following optimization problem:
subject to:
where: M - number of dimensions (equal to 5 in this particular case)
For more information about the Artificial Bee Colony algorithm supported by Deb's rules refer to:
[1] Szczepanski, Rafal, et al. "Comparison of Constraint-handling Techniques Used in Artificial Bee Colony Algorithm for Auto-Tuning of State Feedback Speed Controller for PMSM." ICINCO (1). 2018.
Citar como
Szczepanski, Rafal, et al. “Comparison of Constraint-Handling Techniques Used in Artificial Bee Colony Algorithm for Auto-Tuning of State Feedback Speed Controller for PMSM.” Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics, SCITEPRESS - Science and Technology Publications, 2018, doi:10.5220/0006904002690276.
Información general
- Versión 1.0.0 (6,8 KB)
Compatibilidad con la versión de MATLAB
- Compatible con cualquier versión
Compatibilidad con las plataformas
- Windows
- macOS
- Linux
| Versión | Publicado | Notas de la versión | Action |
|---|---|---|---|
| 1.0.0 |
