Particle Swarm Optimization for Calibrating Agent-Based Models: A Case Study on a Spatially-Explicit Model of Influenza Transmission

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 non-convex 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.