A variant to the dynamic adaptation of parameters in galactic swarm optimization using a fuzzy logic augmentation
Emer Bernal, Oscar Castillo, José Soria, Fevrier Valdez, Patricia Melín · 2018
In this work we propose a variant for the adjustment of parameters in galactic swarm optimization (GSO) using fuzzy logic. GSO is a newly created metaheuristic that uses the movement and distribution of stars and galaxies in the universe as inspiration. In galactic swarm optimization, multiple cycles of exploration and exploitation are used to obtain a better balance between the exploration and the exploitation phases, thus trying to improve the search for the best solutions. In this paper, it is proposed to perform an optimization of the parameters of the membership functions used in an initial fuzzy system called fuzzy galactic swarm optimization 1 (FGSO1) with the aim of improving the results obtained with the FGSO1 using a parameter setting based on experimentation. The proposed method and the original FGSO1 fuzzy system are tested with the CEC-2015 functions with different number of dimensions to be able to appreciate its behavior characteristics from a low number of dimensions to a high number of dimensions.