  This paper presents the Firefighter Optimization (FFO) algorithm as a new
hybrid metaheuristic for optimization problems. This algorithm stems
inspiration from the collaborative strategies often deployed by firefighters in
firefighting activities. To evaluate the performance of FFO, extensive
experiments were conducted, wherein the FFO was examined against 13 commonly
used optimization algorithms, namely, the Ant Colony Optimization (ACO), Bat
Algorithm (BA), Biogeography-Based Optimization (BBO), Flower Pollination
Algorithm (FPA), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Harmony
Search (HS), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Tabu
Search (TS), and Whale Optimization Algorithm (WOA), and across 24 benchmark
functions of various dimensions and complexities. The results demonstrate that
FFO achieves comparative performance and, in some scenarios, outperforms
commonly adopted optimization algorithms in terms of the obtained fitness, time
taken for exaction, and research space covered per unit of time.
