Addressing the limitations of the dung beetle optimization algorithm, such as weak global search capability, slow convergence rate and susceptibility to local optima, this paper innovatively proposes an enhanced dung beetle optimization algorithm based on the golden sine algorithm, named GSDBO algorithm. Firstly, the population initialization is optimized using Tent chaotic mapping and lens imaging reverse learning strategy to generate high-quality initial solutions; secondly, an improved golden sine algorithm is adopted to replace the original position update mechanism of the rolling ball beetle, in order to improve the global search accuracy and convergence speed of the algorithm; finally, by combining the t-distribution mutation strategy and adaptive adjustment mechanism, a dynamic balance is achieved between global exploration and local development capabilities. The experiment used CEC2005 and CEC2020 test functions, combined with Wilcoxon rank sum test, to verify the effectiveness and feasibility of the proposed algorithm. The results showed that the GSDBO algorithm exhibited significant improvements in convergence speed and solution accuracy. In the three engineering optimization problems of cantilever beam design, welding beam design, and robot path planning, this algorithm has obtained the optimal solution, further verifying its effectiveness in solving complex practical problems.