Artificial Bee Colony algorithm (ABC) is one of the most popular algorithms for solving optimization problems in recent years. But it still has the problem of falling into local optimal solution space. In this paper, we propose a novel artificial bee colony algorithm using the global best information and activation strategy to improve the drawbacks of original ABC algorithm. Utilizing the global best solution can speed up the convergence, and the activation strategy is able to provide better exploration capability. The experiment results show that our algorithm is better than the original ABC and related researches one in the uni-modal and multi-modal benchmark functions.