An improved Artificial Bee Colony (ABC) algorithm for function optimization based on central solution was proposed to solve the problem of poor local searching capacity and low accuracy of conventional ABC algorithm. The algorithm combined the advantage of the central solution, which was introduced into the local search process of onlooker bees. Onlooker bees chose some nectar sources with better fitness values using roulette, did the further local optimization based on central solution and updated the value of each dimension of nectar source in every iteration. In order to verify the validity of the proposed algorithm, six standard functions were selected to simulate and compare with the other tow algorithms including ABC algorithm and Best-so-far ABC algorithm, the proposed algorithm greatly improved the quality of solution and reached theoretical optimal value especially for Rastrigin function. The results show that the proposed algorithm has significant improvement on solution accuracy and convergence rate.