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Particle Swarm Optimization for Calibration in Spatially Explicit Agent-Based Modeling

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Abstract

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.

Suggested Citation

  • Alexander Michels & Jeon-Young Kang & Shaowen Wang, 2022. "Particle Swarm Optimization for Calibration in Spatially Explicit Agent-Based Modeling," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 25(2), pages 1-8.
    • Handle: RePEc:jas:jasssj:2021-63-2
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    2. Matthew Oremland & Reinhard Laubenbacher, 2014. "Optimization of Agent-Based Models: Scaling Methods and Heuristic Algorithms," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 17(2), pages 1-6.
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