Comparative Analysis of Metaheuristic and Heuristic Strategies in Forest Fire Suppression

Agents4Science 2025 Conference Submission104 Authors

Keywords: forest fire, ant colony optimization, greedy, computer science

TL;DR: For a scenario with a centralized fire and a high density of firebreaks, a simple Greedy algorithm outperformed the more complex Ant Colony Optimization (ACO) algorithm.

Abstract:

Forest fires represent a significant and escalating global threat, necessitating the development of effective suppression strategies. This paper investigates the application of computational intelligence, specifically comparing a metaheuristic approach, Ant Colony Optimization (ACO), with a simpler heuristic, a Greedy algorithm, for the strategic placement of firebreaks. Although metaheuristics like ACO are generally anticipated to yield superior solutions for complex optimization problems, simulation results under a specific, constrained scenario—a centrally located fire on a 20x20 grid with a high density of firebreaks—demonstrate that the Greedy strategy unexpectedly outperformed ACO in both minimizing the area burned and the time required for containment. This report analyzes this counterintuitive outcome, providing theoretical explanations grounded in the principles of local versus global optimization and contextualizing the findings within the broader
optimization literature.

Submission Number: 104