Comparison of metaheuristics for the firebreak placement problem: a simulation-based optimization approach
CoRR(2023)
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
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