The Whale Optimization Algorithm (WOA) is an effective meta-heuristic. However, whale optimization algorithms tend to converge slowly and are prone to fall into local optimal solutions. Therefore, this paper proposes an algorithm based on the exploration idea of the Aquila Optimizer (AO) to solve global optimization problems. The improved algorithm is inspired by the exploration idea of the Aquila Optimizer. Firstly, Aquilas conduct exploration to expand the search range, thereby enhancing the global search capability and reducing the likelihood of getting trapped in local optima. Secondly, it incorporates the hunting behavior of whales to balance the exploration and exploitation phases of the algorithm. To validate the effectiveness of the algorithm, this study uses benchmark test functions as experimental objects and compares it with other popular metaheuristic algorithms. The experimental results demonstrate that the proposed algorithm in this study has shown good convergence speed, optimization accuracy, and stability.