Artificial bee colony (ABC) algorithm is a relatively new optimization algorithm which has been used to solve many kinds of numerical function optimization problems. It performs well in most cases, however, the solution search equation of artificial bee colony algorithm exists some disadvantages when solving complex functions with high dimensions, such as the convergence speed is not fast enough, easy to fall into local optimum. In order to solve these issues, artificial bee colony algorithm with mixed search equation is proposed. In this algorithm, two different search equations are dynamically used when the employed bees and the onlooker bees search around the neighborhood of the food source. It can keep the diversity of the population and improve the global search ability at the initial generations, and improve the local search ability at a later time. In addition, we use a more robust calculation to determine and compare the quality of alternative solutions. Experiments are conducted on a set of 28 benchmark functions, and the results demonstrate that the new algorithm has fast convergence and high accuracy than several other ABC-based algorithms.