INFO: Efficient Optimizer based on Weighted Mean of Vectors

INFO: An Efficient Optimization Algorithm based on Weighted Mean of Vectors The source codes at: https://aliasgharheidari.com/INFO.html
200 descargas
Actualizado 3 feb 2022

Ver licencia

The source codes of this algorithm are publicly available at https://aliasgharheidari.com/INFO.html. This study presents the analysis and principle of an innovative optimizer named weIghted meaN oF vectOrs (INFO) to optimize different problems. INFO is a modified weight mean method, whereby the weighted mean idea is employed for a solid structure and updating the vectors’ position using three core procedures: updating rule, vector combining, and a local search. The updating rule stage is based on a mean-based law and convergence acceleration to generate new vectors. The vector combining stage creates a combination of obtained vectors with the updating rule to achieve a promising solution. The updating rule and vector combining steps were improved in INFO to increase the exploration and exploitation capacities. Moreover, the local search stage helps this algorithm escape low-accuracy solutions and improve exploitation and convergence. The performance of INFO was evaluated in 48 mathematical test functions, and five constrained engineering test cases. According to the literature, the results demonstrate that INFO outperforms other basic and advanced methods in terms of exploration and exploitation. In the case of engineering problems, the results indicate that the INFO can converge to 0.99% of the global optimum solution. Hence, the INFO algorithm is a promising tool for optimal designs in optimization problems, which stems from the considerable efficiency of this algorithm for optimizing constrained cases. The source codes of this algorithm will be publicly available at https://imanahmadianfar.com. and https://aliasgharheidari.com/INFO.html.

Citar como

Ahmadianfar, Iman, et al. “INFO: An Efficient Optimization Algorithm Based on Weighted Mean of Vectors.” Expert Systems with Applications, Elsevier BV, Jan. 2022, p. 116516, doi:10.1016/j.eswa.2022.116516.

Ver más estilos
Compatibilidad con la versión de MATLAB
Se creó con R2021b
Compatible con cualquier versión
Compatibilidad con las plataformas
Windows macOS Linux

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!
Versión Publicado Notas de la versión
1.0.2

enhance in few lines

1.0.1

cover added

1.0.0