A Gravitational Artificial Bee Colony Optimization Algorithm and Application
Lingling Zhang · 2018
In this paper, a novel Gravitational Artificial Bee Colony (GABC) optimization algorithm was proposed and utilized to the non-supervised pattern recognition problems. In this approach, the gravitational search strategy was introduced into the artificial bee colony algorithm, and a gravitational bee colony was established. The gravitational bee could search the global optimal result under the influence of both gravitational force and colony cooperation, which makes the optimization process more effectively and efficiently. Based on GABC algorithm, an intelligent kernel clustering model was established, in which the clustering center and kernel parameters were combined to be the optimal variable, while the clustering index was used as the objective function. GABC was utilized to find the optimal result of the clustering model. The standard testing functions were used to test the proposed algorithm, and GABC showed high accuracy and convergence speed. Then the testing data and fault samples were utilized to test the performance of GABC based clustering model, and its superiority on effectiveness and efficiency was demonstrated.