Searching saddle points on the potential energy surface is a challenging problem in the rare event. When there exist multiple saddle points, sampling different initial guesses are needed in local search methods in order to find distinct saddle points. In this paper, we present a novel global optimization-based dimer method (GOD) to efficiently search saddle points by coupling ant colony optimization (ACO) algorithm with optimization-based shrinking dimer (OSD) method. In particular, we apply OSD method as a local search algorithm for saddle points and construct a pheromone function in ACO to update the global population. By applying a two-dimensional example and a benchmark problem of seven-atom island on the (111) surface of an FCC crystal, we demonstrate that GOD shows a significant improvement in computational efficiency compared with OSD method. Our algorithm is the first try to apply the global optimization technique in searching saddle points and it offers a new framework to open up possibilities of adopting other global optimization methods.

中文翻译：

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基于全局优化的二分法求鞍点

在极少数情况下，在势能表面上搜索鞍点是一个具有挑战性的问题。当存在多个鞍点时，在局部搜索方法中需要采样不同的初始猜测，以便找到不同的鞍点。在本文中，我们提出了一种新颖的基于全局优化的二聚体方法（GOD），该方法通过结合蚁群优化（ACO）算法和基于优化的收缩二聚体（OSD）方法来有效地搜索鞍点。特别是，我们将OSD方法用作鞍点的局部搜索算法，并在ACO中构造信息素函数以更新全局种群。通过在FCC晶体的（111）表面上应用二维示例和七个原子岛的基准问题，我们证明，与OSD方法相比，GOD在计算效率上有显着提高。我们的算法是首次尝试将全局优化技术应用于搜索鞍点，并且它提供了一个新的框架，以开拓采用其他全局优化方法的可能性。