Kaya, ErsinUymaz, Sait AliKoçer, Barış2021-12-132021-12-1320191868-80711868-808Xhttps://doi.org/10.1007/s13042-018-0878-6https://hdl.handle.net/20.500.13091/808Galactic swarm optimization (GSO) is a new global metaheuristic optimization algorithm. It manages multiple sub-populations to explore search space efficiently. Then superswarm is recruited from the best-found solutions. Actually, GSO is a framework. In this framework, search method in both sub-population and superswarm can be selected differently. In the original work, particle swarm optimization is used as the search method in both phases. In this work, performance of the state of the art and well known methods are tested under GSO framework. Experiments show that performance of artificial bee colony algorithm under the GSO framework is the best among the other algorithms both under GSO framework and original algorithms.eninfo:eu-repo/semantics/closedAccessGalactic Swarm OptimizationArtificial Bee Colony AlgorithmSwarm IntelligenceMetaheuristic Optimization AlgorithmBee Colony AlgorithmDifferential Evolution AlgorithmArtificial Algae AlgorithmParticle SwarmGlobal OptimizationIntelligenceArticle10.1007/s13042-018-0878-62-s2.0-85070677925