A hybrid strategy, the improved Subtraction Average Optimizer (HSABO), is proposed to address the shortcomings of the Subtraction Average Based Optimizer (SABO) in handling complex problems such as poor convergence accuracy and susceptibility to local optimal. Firstly, Cubic mapping is applied for population initialization, thereby improving the overall search potential of the optimization algorithm. Secondly, adaptive T-distribution disturbance is introduced in the loop process. Finally, the golden sine strategy is used to correct the global optimum and population. Eight test functions with single-peak and multi-peak characteristics were used for comparison simulation experiments of various algorithms and improved ablation experiments of three strategies. Findings demonstrate that the three refined strategies effectively improve the performance of the conventional SABO algorithm, and the HSABO algorithm excels in performance compared to other algorithms.