In view of the disadvantages of the standard Artificial Bee Colony （ABC） algorithm such as weak development ability and slow convergence， a new ABC algorithm based on multi-population combination strategy was proposed. Firstly， the different-dimensional coordination and multi-dimensional matching update mechanisms were introduced into the search equation. Then， two combination strategies were designed for the hire bee and the follow bee respectively. The combination strategy was composed of two sub-strategies focusing on breadth exploration and depth development respectively. In the follow bee stage， the population was divided into free subset and non-free subset， and different sub-strategies were adopted by the individuals belonging to different subsets to balance the exploration and development ability of algorithm. The 15 benchmark functions were used to compare the proposed improved ABC algorithm with the standard ABC algorithm and other three improved ABC algorithms. The results show that the proposed algorithm has better optimization performance in both low-dimensional and high-dimensional problems.