Comparison of metaheuristics for the firebreak placement problem: a simulation-based optimization approach

TL;DR

This paper compares metaheuristics, specifically Genetic Algorithm and GRASP, for optimizing firebreak placement to minimize wildfire damage using a simulation-based approach that accounts for fire unpredictability.

Contribution

It introduces a simulation-based optimization method for firebreak placement, applying and comparing Genetic Algorithm and GRASP in a stochastic wildfire context.

Findings

Genetic Algorithm outperforms GRASP in medium to high capacity scenarios.

The approach effectively handles the stochastic nature of wildfires.

Simulation-based optimization provides a viable solution for complex firebreak placement problems.

Abstract

The problem of firebreak placement is crucial for fire prevention, and its effectiveness at landscape scale will depend on their ability to impede the progress of future wildfires. To provide an adequate response, it is therefore necessary to consider the stochastic nature of fires, which are highly unpredictable from ignition to extinction. Thus, the placement of firebreaks can be considered a stochastic optimization problem where: (1) the objective function is to minimize the expected cells burnt of the landscape; (2) the decision variables being the location of firebreaks; and (3) the random variable being the spatial propagation/behavior of fires. In this paper, we propose a solution approach for the problem from the perspective of simulation-based optimization (SbO), where the objective function is not available (a black-box function), but can be computed (and/or approximated) by wildfire simulations. For this purpose, Genetic Algorithm and GRASP are implemented. The final implementation yielded favorable results for the Genetic Algorithm, demonstrating strong performance in scenarios with medium to high operational capacity, as well as medium levels of stochasticity