Although the dung beetle optimization algorithm (DBO) has unique advantages, there are also some problems, such as low convergence accuracy and easy to fall into local optimum. In order to solve these problems, an improved dung beetle optimization algorithm named MSIDBO is proposed to enhance the optimization effect and maintain the balance between global and local search. An adaptive fitness distance balance strategy is proposed, which effectively avoids the dilemma of the algorithm falling into the local optimal solution by optimizing the foraging and stealing behavior of dung beetles. At the same time, the guided learning strategy and the local optimal perturbation scheme are introduced to accelerate the convergence speed of the algorithm and balance the relationship between the local development and global exploration ability of the algorithm. In order to evaluate the performance of MSIDBO algorithm, CEC2017 test function is used for simulation experiments. In three practical engineering design problems, MSIDBO algorithm is used at the same time, and compared with other five optimization algorithms. The results show that MSIDBO algorithm has significant advantages in convergence speed, solution accuracy and stability, which fully verifies its efficiency and reliability in practical application.