A challenge in computational modeling of complex geospatial systems is the amount of time and resources required to tune a set of parameters that reproduces the observed patterns of phenomena of 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 spatially explicit agent-based models (ABMs). We use a spatially explicit ABM of influenza transmission based in Miami, Florida, USA as a case study. Further, 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. The notebook is designed to teach you about Particle Swarm Optimization (PSO) and how you can use it for parameter optimization.