The h-index is a citation-based metric with extensive applications, and several variants have been developed to complement it. This study formulates the optimal growth path (OGP) models of selected h-like indexes, that is, the h-index, g-index, A-index, R-index, and e-index, and analyzes their OGP-allocated strategies of citations. It is argued that the OGP is a useful tool for analyzing the sensitivity of these h-like indexes to citations. Through simulation experiments with both real and random data, the sensitivity of the selected h-like indexes to citations is compared. Interestingly, it is found that the h-index performs the worst according to the OGP. Further, it is shown that combining the h-index with the A-index decreases the sensitivity to the citations of the h-index. In summary, this study provides new insights into how to evaluate scientific outputs based on h-like indexes.

该^ h -index是引用基于广泛的应用，和几个变种指标已经发展到补充。本研究制定了选定的类h指数（即h指数、g指数、A指数、R指数和e指数）的最优增长路径（OGP）模型，并分析了它们的 OGP 分配策略的引用。有人认为 OGP 是分析这些类似h的索引对引用的敏感性的有用工具。通过对真实数据和随机数据的模拟实验，选定h比较类似引用的索引。有趣的是，根据 OGP发现h指数表现最差。此外，还表明将h- index 与A- index 结合会降低对h- index引用的敏感性。总之，这项研究为如何基于h类指标评估科学成果提供了新的见解。