Title :
Binary ant colony algorithm with Balanced search bias
Author :
Hu, Gang ; Xiong, Weiqing ; Jiang, Baochuan ; Yuan, Junliang ; Zhang, Xian
Author_Institution :
Inst. of Comput. Sci. & Technol., Ningbo Univ., Ningbo, China
Abstract :
Binary ant colony algorithm has good performance in the function optimization problem. However, the drawbacks that easy to fall into the local optimization still exist. Through the analysis of “best-so-far” pheromone update rule, we get the lower probability bound under this update rule. Then binary ant colony algorithm with Balanced search bias is proposed. Experiment results have shown that the improved algorithm has good globe search ability and need small iterate times.
Keywords :
optimisation; probability; search problems; balanced search bias; binary ant colony algorithm; function optimization problem; lower probability bound; Algorithm design and analysis; Ant colony optimization; Equations; Mathematical model; Optimization; Probabilistic logic; Search problems; Binary ant Colony Algorithm; Function Optimization; Pheromone Update Rule; Search Bias;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2010 8th World Congress on
Conference_Location :
Jinan
Print_ISBN :
978-1-4244-6712-9
DOI :
10.1109/WCICA.2010.5554964