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.

翻译：本文提出了一种用于优化问题的新型混合元启发式算法——消防员优化（FFO）算法。该算法灵感来源于消防员在灭火行动中常采用的协同策略。为评估FFO的性能，我们进行了大量实验，将FFO与13种常用优化算法进行对比测试，包括蚁群优化（ACO）、蝙蝠算法（BA）、生物地理学优化（BBO）、花授粉算法（FPA）、遗传算法（GA）、灰狼优化器（GWO）、和声搜索（HS）、粒子群优化（PSO）、模拟退火（SA）、禁忌搜索（TS）和鲸鱼优化算法（WOA），并在24个不同维度和复杂度的基准函数上展开实验。结果表明，FFO在获得的适应度、执行耗时以及单位时间内探索的搜索空间方面，均展现出可比性能，并在某些场景下优于常用优化算法。