Metaheuristic-Based Team Coordination of Heterogeneous UAVs for Wildfire Suppression

This paper presents an efficient metaheuristic approach, Hybrid PSO-based Routing and Scheduling with Time constraints (HPSO-RST), to address the coordination of heterogeneous UAV teams for wildfire suppression. Unlike static task scheduling, wildfires are dynamic and critical tasks that demand timely intervention with limited resources. The primary goal of this research is to suppress fires before they surpass the capacity of a single UAV, taking into account heterogeneous time constraints while minimizing total quench time. The HPSO-RST incorporates operators such as velocity and position updates, local search, and mutation within a swarm intelligence framework to effectively explore and exploit the solution space, balancing diversity and convergence. HPSO-RST is successful in generating better particles over iterations. The Monte-Carlo analysis demonstrates that HPSO-RST achieves a 14% higher success rate in managing multiple fires compared to existing approaches, along with reduced mean completion times and minimized destruction of wildlands. The robustness of the HPSO-RST in addressing the NP-complete problem of dynamic wildfire management ultimately enhances system efficiency in managing growing and complex fire tasks.