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Background: Fibroblast growth factor 21 (FGF-21) is an evolutionarily conserved protein that plays multiple roles in metabolic regulation. Over the past two decades, numerous studies have deepened our understanding of its various functions and its pharmacological value. Nevertheless, most clinical trials have not achieved the desired results, which raises issues regarding its clinical value. In this bibliometric analysis, we evaluated the state of FGF-21 research over the last 20 years and identified important topics, achievements, and potential future directions.Methods: Publications related to FGF-21 were collected from the Web of Science Core Collection-Science Citation Index Expanded. HistCite, VOSviewer, and CiteSpace were used for bibliometric analysis and visualization, including the analysis of annual publications, leading countries, active institutions and authors, core journals, co-cited references, and keywords.Results: Altogether, 2,490 publications related to FGF-21 were obtained. A total of 12,872 authors from 2,628 institutions in 77 countries or regions reported studies on FGF-21. The United States of America was the most influential country in FGF-21 research. Alexei Kharitonenkov, Steven A. Kliewer, and David J. Mangelsdorf were the most influential scholars, and endocrinology journals had a core status in the field. The physiological roles, clinical translation, and FGF-21-based drug development were the main topics of research, and future studies may concentrate on the central effects of FGF-21, FGF-21-based drug development, and the effects of FGF-21 on non-metabolic diseases.Conclusion: The peripheral metabolic effects of FGF-21, FGF-21-based drug development, and translational research on metabolic diseases are the three major topics in FGF-21 research, whereas the central metabolic effects of FGF-21 and the effects of FGF-21 on metabolic diseases are the emerging trends and may become the following hot topics in FGF-21 research.

List-based simulated annealing (LBSA) algorithm, which uses list-based cooling scheme to control the change of parameter temperature, was first proposed for traveling salesman problem. This paper extends the application of LBSA algorithm for 0-1 knapsack problem (0-1 KP). A hybrid greedy repair and optimization operator, which combines density-based and value-based greedy repair and optimization operators, is designed to get better balance between intensification and diversification. Extensive experiments were performed to show the effectiveness and parameter robustness of a list-based cooling scheme and to verify the advantage of using hybrid greedy repair and optimization operator. Comparing experiments, which were conducted on small-scale, medium-scale, and large-scale 0-1 KP instances, have shown that LBSA algorithm is better than or competitive with other state-of-the-art metaheuristics.