An improved dung beetle optimization algorithm integrating multiple strategies (MSDBO) is proposed to solve the problems of weak global exploration ability, low convergence accuracy, and easy capture by local optimum solution. Firstly, this study introduces the social learning strategy to guide the dung beetle to update its position, which improves the global exploration ability of the algorithm and avoids the algorithm falling into local optimal. Secondly, the study proposes a direction-following strategy to establish the interaction between the thief and the ball-rolling dung beetle, which improves the accuracy of optimization. Finally, taking into account the performance and time consumption, it introduces environment-aware probability to guide the thief to adopt the direction-following strategy reasonably. Several optimization algorithms are selected and compared with MSDBO. By solving and analyzing 12 benchmark test functions, it is proved that the optimization performance of MSDBO is significantly better than that of the comparison algorithm. The results of pressure vessel design optimization verify the effectiveness of MSDBO in solving practical engineering constraint optimization problems.