Half a billion simulations: evolutionary algorithms and distributed computing for calibrating the SimpopLocal geographical model

TL;DR

This paper presents a novel computational approach combining evolutionary algorithms and distributed computing to efficiently calibrate a complex geographical model simulating early urban settlement evolution, reducing computation time and improving robustness.

Contribution

It introduces an automated calibration pipeline on a computational grid for a geographical model, enabling efficient parameter estimation and validation.

Findings

Identified parameter settings that minimize objective functions.

Reduced the parameter domain for the model.

Demonstrated robustness and efficiency of the calibration method.

Abstract

Multi-agent geographical models integrate very large numbers of spatial interactions. In order to validate those models large amount of computing is necessary for their simulation and calibration. Here a new data processing chain including an automated calibration procedure is experimented on a computational grid using evolutionary algorithms. This is applied for the first time to a geographical model designed to simulate the evolution of an early urban settlement system. The method enables us to reduce the computing time and provides robust results. Using this method, we identify several parameter settings that minimise three objective functions that quantify how closely the model results match a reference pattern. As the values of each parameter in different settings are very close, this estimation considerably reduces the initial possible domain of variation of the parameters. The model is thus a useful tool for further multiple applications on empirical historical situations.