A segmented artificial bee colony algorithm based on synchronous learning factors

Abstract

In this paper, we propose a segmented ABC algorithm based on synchronous learning factors (SABC). For the problem of inferior local search ability and low convergence precision in the artificial bee colony (ABC) algorithm, we use the method of synchronous change learning factors for local search. Then under the guidance of the segmented thought, it updates the quality honey greedily. It improves the efficiency of nectar source updating, enhances the local search ability of artificial bee colony. The six standard test functions are chosen to do the simulation experiments. Compared with the other three experiments, the results show that SABC has a significant improvement in the convergence speed and searching optimal value.