Abstract: Due to its simpler operation, less control parameters, and stronger robustness, artificial bee colony algorithm (ABC) has been becoming a hot topic of swarm intelligence. However, there still exist some shortcomings in ABC algorithm, such as low convergence rate and easily falling into local optimization. Hence, this paper proposes a quick self adaptive artificial bee colony algorithm (QAABC). First, the strategies of selection and search in the standard ABC are improved to enhance the convergence rate and the optimization precision. Then, in order to maintain the population diversity, an effective mutation is utilized when the particles beyond the boundary. Finally, the comparisons between the proposed approach with other algorithms, Standard ABC and ABCP, are made on 5 test functions. Moreover, the QAABC algorithm is also applied to the minimization of the cost in the pressure vessel design. The above experiments show the effectiveness of the improved algorithm.