The Wang-Landau (WL) algorithm is a recently developed stochastic algorithm computing densities of states of a physical system, and also performing numerical integration in high dimensional spaces. Since its inception, it has been used on a variety of (bio-)physical systems, and in selected cases, its convergence has been proved. The convergence speed of the algorithm is tightly tied to the connectivity properties of the underlying random walk.

As such, we propose an efficient random walk that uses geometrical information to circumvent the following inherent difficulties: avoiding overstepping strata, toning down concentration phenomena in high-dimensional spaces, and accommodating multidimensional distributions. These improvements are especially well suited to improve calculations on a per basin basis – included anharmonic ones.

Experiments on various models stress the importance of these improvements to make WL effective in challenging cases. Altogether, these improvements make it possible to compute density of states for regions of the phase space of small biomolecules.

Wang-Landau（WL）算法是最近开发的一种随机算法，用于计算物理系统的状态密度，并且还可以在高维空间中执行数值积分。自从它诞生以来，它已经在各种（生物）物理系统上使用，并且在某些情况下，已经证明了它的收敛性。该算法的收敛速度与底层随机游走的连通性紧密相关。

因此，我们提出了一种有效的随机行走方法，该方法利用几何信息来规避以下内在困难：避免超层，降低高维空间中的集中现象以及适应多维分布。这些改进特别适合改进每个流域（包括非谐波流）的计算。

在各种模型上进行的实验强调了这些改进对于使WL在具有挑战性的情况下有效的重要性。总而言之，这些改进使得有可能计算出小生物分子相空间区域的状态密度。