Mutation strategy plays a decisive role on the success of the differential evolution algorithm(DE). However, the direction information has not been fully exploited in the design of DE and the balance between the evolution speed and the population diversity cannot be well handled so far. In this paper, it explores a novel direction information which is generated by the selection operation and it's directive effect on the mutation operation. On this basis, it proposes an evolution direction-based mutation strategy "DE/current-to-pbest/1/Gvector" and an improved differential evolution algorithm based on adaptive differential evolution algorithm(JADE) for comparison. We name our algorithm as DVDE and compare it with five state-of-the-art adaptive DE variants(JADE, SaDE, CoDE, jDE, EPSDE), using 12 standard numerical benchmarks taken from the IEEE Congress on Evolutionary Computation 2005. The simulation results indicate that the average performance of the DVDE is better than those of all other competitors, especially for the unimodal functions. The experimental results also illustrate that the using of the evolution direction is helpful to improve the algorithm's convergence speed, maintain the population, and effectively avoid premature convergence problem.