Optimization algorithms play as a crucial role in solving complex real-world problems, where achieving global optimal in high-dimensional spaces remains challenging. This article presents a novel hybrid algorithm, which combining the Modified Adaptive Bats Sonar Algorithm (MABSA) with the Squirrel Search Algorithm (SSA). This synergistic approach is designed to improve conjunction speed and solution accuracy, particularly in high-dimensional in solving optimization problems using evolutionary algorithms. To evaluate the performance of this enhanced MABSA, experimental evaluations are conducted using a comprehensive suite of seven single objective benchmark test functions to assess the performance of MABSA-SSA against the original MABSA. Notably, the SSA component enhances the capability and improves their exploration diversity. The results demonstrate that MABSA-SSA consistently better solution quality compared to the original MABSA alone. The comparative analysis demonstrates that the enhancement of MABSA exhibits superior performance in avoiding local optima and maintaining solution diversity. As conclusion, the enhancement of MABSA-SSA approach represent a significant advancement in benchmark optimization fields, providing a foundation for future developments in metaheuristic optimization and potential addressing the complex optimization challenges.