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<p>This paper presents the Firefighter Optimization (FFO) algorithm as a new metaheuristic for optimization problems that stems inspiration from the collaborative strategies often deployed by firefighters in firefighting activities. Such strategies include adaptive response to changing conditions, coordination among multiple firefighters (i.e., agents) to converge on a common goal, balancing exploration and exploitation by maintaining diversity within the search space and adapting its parameters to navigate complex landscapes. 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, as well as 10 standard functions and 4 real engineering problems from the CEC 2020 suite. 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. More specifically, FFO ranked first in the Distance per Unit Time metric and maintained a top 5 performance in higher dimensions (i.e., 20D and 50D).</p>

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The firefighter algorithm for optimization problems

  • 期刊论文

  • 发表日期：

    2025年2月22日

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  • M. Z. Naser,
  • Ahmad Z. Naser

摘要

This paper presents the Firefighter Optimization (FFO) algorithm as a new metaheuristic for optimization problems that stems inspiration from the collaborative strategies often deployed by firefighters in firefighting activities. Such strategies include adaptive response to changing conditions, coordination among multiple firefighters (i.e., agents) to converge on a common goal, balancing exploration and exploitation by maintaining diversity within the search space and adapting its parameters to navigate complex landscapes. 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, as well as 10 standard functions and 4 real engineering problems from the CEC 2020 suite. 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. More specifically, FFO ranked first in the Distance per Unit Time metric and maintained a top 5 performance in higher dimensions (i.e., 20D and 50D).