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

Author(s): Alexander Michels

The notebook is designed to teach you about Particle Swarm Optimization (PSO) and how you can use it for parameter optimization. Particle Swarm Optimization (PSO) was first introduced in 1995 by James Kennedy and Russell Eberhart. The algorithm began as a simulation of social flocking behaviors like those exhibited by flocks of birds and schools of fish, specifically of birds searching a cornfield, but was found to be useful for training feedforward multilayer pernceptron neural networks. Since then, PSO has been adapted in a variety of ways and applied to problems including wireless-sensor networks, classifying biological data, scheduling workflow applications in cloud computing environments, Image classification and power systems. In this notebook we explore PSO's usefulness for calibration, with a focus on spatially-explicit agent-based models (ABMs). Publication:

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