A Comprehensive Study of Parameters Analysis for Galactic Swarm Optimization
Ersin Kaya · International Journal of Intelligent Systems and Applications in Engineering · 2021
The galactic swarm optimization algorithm is a metaheuristic approach inspired by the motion and behavior of stars and galaxies.It is a framework that can use basic metaheuristic search methods.The method, which has a two-phase structure, performs exploration in the first phase and exploitation in the second phase.GSO tries to find the best solution in the search space by repeating these two phases for the specified number of times.In this study, the analysis of maximum epoch number (EPmax), the number of iterations in the first phase (𝐿 1 ), and the number of iterations in the second phase (𝐿 2 ) parameters, which determine the exploration and exploitation balance in the GSO method, was performed.15 different parameter sets consisting of different values of these three parameters were created.The methods with 15 different parameter sets were performed at 30 independent runs.The methods were analyzed using 26 benchmark functions.The functions are tested in 30, 60, and 100 dimensions.Detailed results of the analysis were presented in the study, and the results obtained were also evaluated statistically.