Boosting galactic swarm optimization with ABC

作     者：Kaya, Ersin Uymaz, Sait Ali Kocer, Baris 

作者机构：Konya Tech Univ Fac Engn & Nat Sci Dept Comp Engn Konya Turkey 

出 版 物：《INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS》 (国际机器学习与控制论杂志)

年 卷 期：2019年第10卷第9期

页      面：2401-2419页

核心收录：

学科分类：08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 081102[工学-检测技术与自动化装置] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Galactic swarm optimization Artificial bee colony algorithm Swarm intelligence Metaheuristic optimization algorithm 

摘      要：Galactic 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.