鲸鱼优化器（WOA）是一种有效的元启发式算法。但鲸鱼优化算法往往收敛速度慢并且容易陷入局部最优解。 因此，提出了基于天鹰优化算法（AO）的勘探思想改进的算法来解决全局优化问题。改进后的算法受到了天鹰优化算法勘探 思想的启发，首先，天鹰先进行勘探，扩大了搜索范围，以提高全局搜索能力并降低陷入局部最优的可能性；其次，鲸鱼游动围 猎，以此平衡算法勘探和开发两个阶段。为了验证算法的有效性，本文算法以基准测试函数为实验对象，与其他流行的元启发 式算法进行比较。实验结果证明本文所提出的算法具有良好的收敛速度、寻优精度和稳定性。

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.