Aiming at the shortcomings of Artificial Bee Colony (ABC) algorithm, which is not easy to jump out of the local optimal value, a Multiple Interactive Artificial Bee Colony (MIABC) algorithm was proposed. The proposed algorithm was based on the basic ABC algorithm, involved the random neighborhood search strategy and the cross-dimensional search strategy, and improved the treatment when bees exceed the limit, so the search way of the algorithm became various, the algorithm itself had stronger bound and it's hard to trap in the local optimal value. Meanwhile, the convergence analysis and performance test were carried out. The simulation result based on five kinds of classic benchmark functions and experimental results for time complexity show that comparing with the standard ABC algorithm and basic Particle Swarm Optimization (PSO), this proposed method has faster convergence speed which is increased by about 30% and 65% at 1E-2 accuracy and better search precision, besides, it has significant advantages in solving high dimensional problems.