Finding feasible solutions to real-world problems is a crucial task. Metaheuristic algorithms are widely used in many fields due to the variety of solutions they can produce. The grey wolf optimizer (GWO) is a relatively novel population-based metaheuristic algorithm that has been shown to have good optimization performance. However, due to the insufficient diversity of wolves in some cases, this approach can lead to locally optimal situations. Therefore, this paper proposes a grey wolf optimization method based on a beetle antenna strategy (BGWO) that gives the leader wolf a sense of hearing to improve the global search ability and reduce unnecessary searches. In addition, to balance exploration and exploitation, a nonlinear dynamic control parameter update strategy based on the cosine function is proposed. To evaluate the performance of the proposed BGWO, this paper uses 23 standard benchmark functions to test the method in different dimensions. Moreover, four well-known engineering problems are used to evaluate the ability of the proposed algorithm to obtain real-world problem solutions. The experimental results show that BGWO has superior performance and is competitive with many state-of-the-art algorithms in terms of solution accuracy, convergence rate, and stability.

找到现实问题的可行解决方案是一项至关重要的任务。元启发式算法由于可以产生多种解决方案而被广泛用于许多领域。灰太狼优化器（GWO）是一种相对新颖的基于种群的元启发式算法，已被证明具有良好的优化性能。但是，由于在某些情况下狼的多样性不足，因此这种方法可能导致局部最佳情况。因此，本文提出了一种基于甲虫天线策略（BGWO）的灰太狼优化方法，该方法使头狼具有听觉，从而提高了全局搜索能力并减少了不必要的搜索。此外，为平衡勘探与开发，提出了一种基于余弦函数的非线性动态控制参数更新策略。为了评估所提出的BGWO的性能，本文使用23个标准基准函数在不同维度上测试该方法。此外，使用四个众所周知的工程问题来评估所提出算法获得实际问题解决方案的能力。实验结果表明，BGWO具有出色的性能，并且在解决方案准确性，收敛速度和稳定性方面都可以与许多最新算法竞争。