Grey wolf optimizer (GWO), which is inspired by the social behaviours of grey wolf packs, is a nature-inspired and population-based algorithm. The GWO technique has the advantage of conceptual simplicity and shows good results for solving various real-world problems. However, this technique has the drawback of premature convergence and is prone to stagnation in local optima. The leadership hierarchy is the paramount characteristic of the GWO and influences its searching precision. Therefore, a grey wolf optimizer with enhanced hierarchy (GWO-EH) is proposed to overcome these deficiencies. Firstly, fitness-based self-adaptive weight coefficients are introduced to better imitate the hierarchy of the grey wolves, which also have a positive effect on the convergence speed. Then, we propose an improved position-updating equation to enhance the leadership of the high-ranking wolves, whereby the global exploration ability of the GWO is strengthened. Finally, the strategy of repositioning wolves around the leading wolves is designed to keep a perfect balance between exploration and exploitation. The search ability of the GWO-EH is thoroughly compared with the GWO itself, some promising GWO variants, and several well-established algorithms on twenty-three widely used benchmark functions. Empirical studies reveal that GWO-EH has a competitive overall performance according to the average value (standard deviation), Wilcoxon rank-sum test results, and convergence curve. Moreover, our method is applied to address the wireless sensor network coverage optimization problem, and the applicability and validity of the GWO-EH are indicated by the experimental results.

灰狼优化程序（GWO）受灰狼背包的社会行为的启发，是一种自然灵感和基于种群的算法。GWO技术具有概念简单的优点，并显示出解决各种实际问题的良好结果。但是，该技术具有过早收敛的缺点，并且在局部最优中易于停滞。领导层是GWO的最重要特征，并影响其搜索精度。因此，提出了一种具有增强层次结构的灰狼优化器（GWO-EH），以克服这些缺陷。首先，引入基于适应度的自适应权重系数以更好地模仿灰狼的等级，这也对收敛速度产生积极影响。然后，我们提出了一个改进的位置更新方程，以增强高阶狼的领导力，从而增强了GWO的全球勘探能力。最后，将狼重新定位在主要狼周围的策略旨在在勘探和开发之间保持完美的平衡。将GWO-EH的搜索能力与GWO本身，一些有前途的GWO变体以及在23种广泛使用的基准函数上建立的几种算法进行了彻底比较。实证研究表明，根据平均值（标准偏差），Wilcoxon秩和检验结果和收敛曲线，GWO-EH的总体性能具有竞争力。而且，我们的方法被应用于解决无线传感器网络覆盖优化问题，