We have developed a global optimization program named PGA based on particle swarm optimization algorithm coupled with genetic operators for the structures of atomic clusters. The effectiveness and efficiency of the PGA program can be demonstrated by efficiently obtaining the tetrahedral Au₂₀ and double-ring tubular B₂₀, and identifying the ground state ${\text{ZrSi}}_{17–20}^{-}$ clusters through the comparison between the simulated and the experimental photoelectron spectra (PESs). Then, the PGA was applied to search for the global minimum structures of ${\mathrm{Mg}}_n^{-}$ (n = 3–30) clusters, new structures have been found for sizes n = 6, 7, 12, 14, and medium-sized 21–30 were first determined. The high consistency between the simulated spectra and the experimental ones once again demonstrates the efficiency of the PGA program. Based on the ground-state structures of these ${\mathrm{Mg}}_n^{-}$ (n = 3–30) clusters, their structural evolution and electronic properties were subsequently explored. The performance on Au₂₀, B₂₀, ${\text{ZrSi}}_{17–20}^{-}$, and ${\mathrm{Mg}}_n^{-}$ (n = 3–30) clusters indicates the promising potential of the PGA program for exploring the global minima of other clusters. The code is available for free upon request.

中文翻译：

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PGA：一种基于遗传算子的新型粒子群优化算法，用于集群的全局优化


我们开发了一个名为 PGA 的全局优化程序，该程序基于粒子群优化算法与原子簇结构的遗传运算符相结合。PGA 计划的有效性和效率可以通过有效地获得四面体 Au₂₀ 和双环管状 B₂₀，并通过模拟和实验光电子谱 （PES） 之间的比较来识别基态 ${\text{ZrSi}}_{\mathrm{17-20}\mathrm{}}^{-}$ 团簇来证明。然后，应用 PGA 搜索 ${\mathrm{Mg}}_n^{-}$（n = 3-30）簇的全局最小结构，发现了大小 n = 6、7、12、14 的新结构，并首次确定了中型 21-30。模拟光谱和实验光谱之间的高度一致性再次证明了 PGA 程序的效率。基于这些 ${\mathrm{Mg}}_n^{-}$ （n = 3–30） 团簇的基态结构，随后探索了它们的结构演变和电子特性。在 Au₂₀、B₂₀、${\text{ZrSi}}_{\mathrm{17-20}\mathrm{}}^{-}$ 和 ${\mathrm{Mg}}_n^{-}$ （n = 3-30） 簇上的性能表明 PGA 程序在探索其他星簇的全局最小值方面具有广阔的潜力。该代码可应要求免费提供。