Performance analysis of Galactic Swarm Optimization with Tree Seed Algorithm
Ersin Kaya Oğuzhan Uymaz Sedat Korkmaz Eyüp Sıramkaya Mustafa Servet Kıran
AbstractThe Galactic Swarm Optimization (GSO) is a novel method inspired by the movements of stars and star clusters. The GSO is a framework that uses the optimization methods known in the literature. GSO has a two-stage structure. In the first stage, the optimization method identifies possible good solutions by scanning the search space. In the second stage, the best solution is tried to be found by using possible good solutions. In the original GSO study, Particle Swarm Optimization (PSO) was used as an optimization method in both stage. In this study, Tree Seed Algorithm (TSA), a new optimization method in the literature, is used in GSO framework instead of PSO. In the experimental study, the performance of the GSO_TSA model has been investigated on numeric benchmark functions and obtained results are compared with GSO_PSO model.