Optimizing Fire Perimeter Geometry in Cellular Automata Wildfire Models Using Ant Colony Optimization

Simulating wildfires in large areas is commonly achieved by methods based on cellular automata (CA), especially when the available computational resources are limited. However, the discrete nature of CA, in both the space and time domains, often leads to systematic distortions and artifacts in the generated fire perimeter geometry. This article presents a hybrid wildfire simulation framework that integrates a two-dimensional CA model with an ant colony optimization (ACO) algorithm to improve fire perimeter shape representation. The proposed approach employs ACO to iteratively adjust the coefficients integrated into the local CA transition rules, under varying environmental and forestry fuel conditions. This optimization step corrects geometric inconsistencies arising from the underlying nature of a CA model, while preserving their computational advantages. The hybrid implementation is evaluated through simulation experiments under varying environmental and fuel conditions, demonstrating improved agreement between simulated and reference fire perimeters, compared to a baseline CA model.