Antarctic Icefish Optimization Algorithm

To address issues such as premature convergence, uneven dimensional coupling, and local minima trapping in high-dimensional complex optimization problems, this paper proposes a swarm optimization algorithm based on the coupling of a non-equilibrium low-temperature dissipative structure and an information diffusion field. Inspired by the adaptation mechanism of Antarctic icefish in extreme low-temperature, high-dissolved-oxygen environments, the algorithm constructs a free energy-driven non-equilibrium evolution model, introduces a structural order parameter and a phase transition determination mechanism, designs an information oxygen diffusion field coupling term, and establishes a direction-dependent memory viscosity tensor and a curvature-tuned cooling and contraction mechanism. Instead of aiming at a single fitness decrease, the algorithm achieves swarm self-organized evolution by minimizing the free energy function. Theoretical analysis shows that this algorithm can be considered a discrete dissipative dynamic system with a stable equilibrium structure and adaptive step-size contraction characteristics. This paper systematically presents the mathematical model, update equations, dynamic interpretation, and convergence discussion.