The Slime Mould method (SMA) successfully employs exploration and exploitation to arrive at an optimal or nearly optimal solution, additionally, the slime mold algorithm (SMA) has gained popularity recently in the field of function optimization. However, the SMA’s exploitation and exploration are limited. To address this issue, we look into an adaptive method to determine whether or not to employ opposition-based learning (OBL). Occasionally, the OBL is employed to enhance the level of exploration. Furthermore, it optimizes the exploitation by substituting the optimal search agent in the position updating for a random search agent. The adaptive opposition slime mold algorithm (AOSMA) is the proposed method. Thirteen test functions are used in the qualitative and quantitative examination of AOSMA. The comparison of the raised AOSMA on thirteen benchmark functions with selected state-of-the-art methods demonstrates that the developed algorithm performs effectively and competitively.