Marlin Fish Optimization Algorithm

Swarm intelligence optimization algorithms have been widely applied in engineering optimization, machine learning parameter tuning, and solving complex functions in recent years. This paper proposes an optimization algorithm based on the hunting behavior of marlin—the Marlin Fish Optimization Algorithm (MFOA). MFOA achieves an organic combination of global search and local exploitation by simulating the global navigation, local pursuit, and prey simulation behaviors of marlin. The mathematical model of the algorithm is constructed in detail, including formulas for individual position updates, velocity updates, dynamic energy regulation, and population diversity assessment. Theoretical analysis demonstrates the advantages of MFOA in terms of global search capability, local exploitation accuracy, and swarm intelligence, providing an effective method for continuous, discrete, and complex hybrid optimization problems.