Longfin Tuna Optimization Algorithm

To address the common problems of premature convergence, oscillatory instability, and local extremum trapping in high-dimensional complex optimization problems, this paper proposes a swarm intelligence optimization method based on the coupling of fluid dynamics and geometric information—the Longfin Tuna Optimization Algorithm. Starting from the behaviors of longfin tuna in ocean cruising, swarm hunting, and energy regulation, the algorithm constructs five core mechanisms: inertial hydrodynamic tensor, information vortex field, energy and curvature coupling model, reversible and irreversible dual-mode dynamics, and adaptive submanifold cruising mechanism. Unlike traditional swarm intelligence algorithms that rely on single-position difference-driven updates, this algorithm introduces a second-order trajectory dynamic structure and local curvature geometric perception, achieving a structural upgrade at the dynamic system level. By establishing a unified dynamic update equation, the algorithm theoretically possesses stronger global exploration capabilities and local convergence stability. This paper details the mathematical modeling process, algorithm flow, and complexity analysis, and discusses its convergence mechanism and theoretical properties.