In order to solve the problem of low convergence speed and low convergence accuracy of an artificial bee colony algorithm, an improved artificial bee colony algorithm based on ranking selection and elite guidance is proposed. The probability selection method of onlooker bees is weak to select the elite individual when the fitness value is changing, therefore, a ranking selection method is proposed to replace that of probability selection for improving the convergence speed of the algorithm. To improve the search efficiency, two new neighborhood search equations for emplyed bees and onlooker bees respectively are proposed by using the elite guidance. Compared with other artificial bee colony algorithms, the results show that the proposed algorithm can effectively improve the convergence speed and convergence accuracy.