Galactic swarm optimization with adaptation of parameters using fuzzy logic for the optimization of mathematical functions

Abstract

In this paper the Galactic Swarm Optimization (GSO) algorithm with the use of fuzzy systems for the adaptation of the parameters in the GSO algorithm is proposed. This algorithm is inspired by the movement of stars, galaxies and superclusters of galaxies under the force of gravity. The GSO algorithm uses multiple cycles of exploration and exploitation phases to achieve a balance between exploring new solutions and exploiting existing solutions. In this work different fuzzy systems were designed for the dynamic adaptation of the c3 and c4 parameters to measure the operation of the algorithm with 7 mathematical functions with different number of dimensions. A statistical comparison was made between the different variants to test the performance of the method applied to optimization problems.