Abstract: When using traditional artificial bee colony algorithm to optimize the multi-dimensional and multimodal problems, usually only the local optimal solutions are obtained, and the rate of convergence is slow when the classical algorithm is used to handle the single modal problems. In this paper, we propose an artificial bee colony algorithm with double learning ability, which are called DLABC. This algorithm improves the self-learning ability of its optimal value for the individual, and improves the social learning ability of optimal value for other individuals in the colony, while it is searching the bee source neighborhood locally. Learning weight factor is introduced to balance the local search and global search of colony, and it changes dynamic with the numbers of iteration, which can avoid premature convergence and accelerate the rate of convergence. Finally, the experimental evaluations show that DLABC algorithm is more efficient than other improved artificial bee colony algorithms.