Galactic swarm optimization using artificial bee colony algorithm

Galactic swarm optimization (GSO) algorithm is a novel meta-heuristic algorithm inspired by the motion of stars, galaxies and superclusters of galaxies under the influence of gravity. The GSO algorithm utilizes multiple cycles of exploration and exploitation in two levels. The first level covers the exploration, and different subpopulations of the candidate solutions are used for exploring the search space. The second level covers the exploitation, and best solutions obtained from the subpopulations are considered as a superswarm and used for exploiting the search space. The first implementation of GSO algorithm was presented by using particle swarm optimization algorithm (PSO) algorithm on both first and second levels. This study presents the preliminary results of an implementation of GSO algorithm by using artificial bee colony (ABC) algorithm on the first level and PSO algorithm on the second level. Due to the better exploration characteristics of ABC algorithm over PSO algorithm, this suggestion covers the usage of ABC algorithm on the first level, and the usage of PSO algorithm on the second level. The proposed approach is tested on 20 well-known online available benchmark problems and preliminary results are presented. According to the experimental results, the proposed approach achieves more successful results than the basic GSO approach.