To address the limitations of conventional optimization algorithms in Sensor-less Adaptive Optics (SLAO) systems, particularly the slow convergence and limited global search efficiency of Stochastic Parallel Gradient Descent (SPGD) algorithm and meta heuristic optimization algorithms, an Improved Dung Beetle Optimizer (IDBO) algorithm proposed by combining the Dung Beetle Optimizer (DBO) algorithm and the Osprey Optimization Algorithm (OOA) to significantly enhance wavefront correction in SLAO systems, achieving effective atmospheric turbulence suppression without the need for wavefront sensors. While the DBO algorithm is known for its strong optimization ability and fast convergence speed, it suffers from an imbalance between global exploration and local exploitation. To address this issue, we incorporate the Osprey optimization algorithm strategy to improve global exploration and enhance the local exploitation ability.The proposed methodology involves a model of an adaptive optics system using a 32-element deformable mirror for wavefront correction. The IDBO algorithm integrates the OOA to enhance the traditional Dung Beetle Optimizer (DBO), increasing population diversity and global search capabilities. Performance evaluations were conducted under various turbulence levels, utilizing wavefront aberrations as correction targets. We performed comprehensive comparisons of DBO, SPGD, and SA-SPGD algorithms, focusing on convergence speed, correction efficiency, and local extrema resistance. To evaluate the dynamic correction performance of the IDBO algorithm under different intensities of atmospheric turbulence, we conducted 20 simulations of the IDBO algorithm for atmospheric turbulence correction, with each numerical simulation introducing initial atmospheric turbulence of varying intensities.Numerical simulations results show notable improvements at an initial RMS value of 0.825 4, the IDBO algorithm achieves wavefront correction speeds that are approximately 14.0, 3.5, and 1.3 times faster than DBO, SPGD, and SA-SPGD, respectively. Under higher turbulence (initial RMS of 1.772 1), IDBO maintains superior performance with speeds 7.4, 1.1, and 0.8 times faster than the same algorithms. The system consistently delivers an 80% improvement in correction speed across different turbulence conditions while ensuring equivalent correction efficacy. IDBO also demonstrates enhanced convergence stability and robustness, significantly reducing the likelihood of entrapment in local extrema. The IDBO algorithm performs excellently under both weak and strong turbulence conditions. This hybrid algorithm has the advantage of rapidly correcting aberrations caused by turbulence of different intensities, resulting in better adaptive correction capability. The qualitative simulation results show the phase distribution before and after correction under weak and strong turbulence conditions.The integration of the OOA algorithm crucially improves algorithm performance by preserving diversity in the dung beetle population, averting premature convergence to local optimum solutions. The numerical simulations validate that the IDBO-based SLAO system outperforms mainstream algorithms in real-time control applications. With convergence speed, robustness, and dynamic correction performance amidst evolving turbulent conditions, IDBO algorithm presents a viable solution for wavefront correction in SLAO systems. This advancement offers a new approach to overcoming challenges in sensor-less adaptive optics, highlighting potential applications where traditional systems may falter. In the future, we intend to develop a high-performance processing platform based on FPGA and GPU, and apply the IDBO algorithm to dynamic aberration correction experiments.