Grey Wolf Optimizer (GWO) is a new swarm intelligence algorithm mimicking the behaviors of grey wolves. Its abilities include fast convergence, simplicity and easy realization. It has been proved its superior performance and widely used to optimize the continuous applications, such as, cluster analysis, engineering problem, training neural network and etc. However, there are still some binary problems to optimize in the real world. Since binary can only be taken from values of 0 or 1, the standard GWO is not suitable for the problems of discretization. Binary Grey Wolf Optimizer (BGWO) extends the application of the GWO algorithm and is applied to binary optimization issues. In the position updating equations of BGWO, the $a$ parameter controls the values of $A$ and $D$, and influences algorithmic exploration and exploitation. This paper analyzes the range of values of $AD$ under binary condition and proposes a new updating equation for the $a$ parameter to balance the abilities of global search and local search. Transfer function is an important part of BGWO, which is essential for mapping the continuous value to binary one. This paper includes five transfer functions and focuses on improving their solution quality. Through verifying the benchmark functions, the advanced binary GWO is superior to the original BGWO in the optimality, time consumption and convergence speed. It successfully implements feature selection in the UCI datasets and acquires low classification errors with few features.

灰狼优化程序（GWO）是模仿灰狼行为的一种新的群体智能算法。它的功能包括快速收敛，简单和易于实现。它已被证明具有优越的性能，并广泛用于优化连续应用程序，例如聚类分析，工程问题，训练神经网络等。但是，在现实世界中仍然存在一些要优化的二进制问题。由于只能从0或1取二进制值，因此标准GWO不适合离散化问题。二进制灰狼优化器（BGWO）扩展了GWO算法的应用，并应用于二进制优化问题。在BGWO的位置更新方程中，$\mathrm{一种}$ 参数控制的值 $\mathrm{一种}$ 和 $d$，并影响算法的探索和开发。本文分析了值的范围$\mathrm{一种}d$ 在二元条件下，提出了一个新的更新方程 $\mathrm{一种}$参数以平衡全局搜索和本地搜索的能力。传递函数是BGWO的重要组成部分，对于将连续值映射为二进制值至关重要。本文包括五个传递函数，并着重于提高其解决方案质量。通过验证基准功能，先进的二进制GWO在优化性，时间消耗和收敛速度方面优于原始BGWO。它成功地在UCI数据集中实现了特征选择，并以较少的特征获得了较低的分类错误。