Pomfry Fish Optimization Algorithm

Swarm optimization algorithms have become an important method for solving complex optimization problems in recent years. This paper proposes a novel swarm optimization algorithm—the Pomfry Fish Optimization Algorithm (PFOA). This algorithm achieves a balance between global search and local exploitation by simulating the swarm foraging behavior, local risk avoidance strategies, and dynamic energy consumption mechanisms of pomfry. This paper establishes the mathematical model of the algorithm, details the individual representation, energy update, cooperation mechanism, local learning, and multi-strategy update methods, and presents the complete iterative process. Theoretical analysis demonstrates the algorithm's advantages in maintaining population diversity, improving local search accuracy, and escaping local optima. This algorithm can be widely applied to continuous optimization, discrete combinatorial optimization, and multi-objective optimization problems.