An artificial bee colony algorithm improved by a search strategy based on adaptive selection

In order to solve the problems of slow convergence and low accuracy of the basic Artificial Bee Colony algorithm, an adaptive selection strategy using population information is proposed in this paper. The search strategy is controlled by using the change rate of focusing distance, which not only ensures the utilization of the population information, but also improves the utilization of the best individual information by the algorithm. By comparing the improved algorithm with the basic algorithm through experiments, the results show that the improved algorithm has improved the search performance and convergence performance.