期刊名称：Journal of Artificial Societies and Social Simulation
印刷版ISSN：1460-7425
出版年度：2022
卷号：25
期号：2
DOI：10.18564/jasss.4796
语种：English
出版社：University of Surrey, Department of Sociology
摘要：A challenge in computational Agent-Based Models (ABMs) is the amount of time and resources required to tune a set of parameters for reproducing the observed patterns of phenomena being modeled. Well-tuned parameters are necessary for models to reproduce real-world multi-scale space-time patterns, but calibration is often computationally intensive and time consuming. Particle Swarm Optimization (PSO) is a swarm intelligence optimization algorithm that has found wide use for complex optimization including nonconvex and noisy problems. In this study, we propose to use PSO for calibrating parameters in ABMs. We use a spatially explicit ABM of influenza transmission based in Miami, Florida, USA as a case study. Furthermore, we demonstrate that a standard implementation of PSO can be used out-of-the-box to successfully calibrate models and out-performs Monte Carlo in terms of optimization and efficiency.
