Artificial Bee Colony Algorithm Based on Information Learning

• Published in: IEEE transactions on cybernetics Vol. 45; no. 12; pp. 2827 - 2839
• Main Authors: Gao, Wei-Feng, Huang, Ling-Ling, Liu, San-Yang, Dai, Cai
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Animals; Appetitive Behavior; Artificial bee colony algorithm; Bees; Cluster Analysis; Clustering algorithms; clustering partition; Cooperation; Cybernetics; Division; Equations; Labor; Learning; Machine Learning; Mathematical model; Models, Biological; search equation; search mechanism; Search problems; Searching; Sociology; Statistics; Swarm intelligence; Artificial bee colony algorithm; search mechanism; search equation; clustering partition
• ISSN: 2168-2267; 2168-2275
• DOI: 10.1109/TCYB.2014.2387067

Inspired by the fact that the division of labor and cooperation play extremely important roles in the human history development, this paper develops a novel artificial bee colony algorithm based on information learning (ILABC, for short). In ILABC, at each generation, the whole population is divided into several subpopulations by the clustering partition and the size of subpopulation is dynamically adjusted based on the last search experience, which results in a clear division of labor. Furthermore, the two search mechanisms are designed to facilitate the exchange of information in each subpopulation and between different subpopulations, respectively, which acts as the cooperation. Finally, the comparison results on a number of benchmark functions demonstrate that the proposed method performs competitively and effectively when compared to the selected state-of-the-art algorithms.