Application of Triple Glazing Thickness Optimization Based on Artificial Fish Swarm Algorithm

In response to the growing demand for energy-efficient building designs in cold northern regions, this study explores the optimization of triple glazing thickness to maximize the transmission of solar energy within the wavelength range of 300-2000 nm. The optimization employs the Artificial Fish Swarm Algorithm (AFSA), which is used to iteratively determine the optimal combination of glass thicknesses. A transmittance model of solar radiation through triple glazing is established, where the thicknesses of the three glass layers are treated as variables. The study shows that the optimized thickness combination significantly enhances the indoor heating effect by maximizing solar energy incidence, offering theoretical support and practical guidance for energy-saving building designs in cold climates. Parameter settings of the AFSA, including perception range, step size, and crowding factor, are analyzed to understand their influence on optimization performance. The results suggest that AFSA efficiently addresses complex optimization problems, demonstrating its potential in optimizing material properties for energy-efficient applications. Future directions include multi-objective optimization considering additional factors such as thermal insulation and cost, algorithmic improvements to enhance search accuracy, and experimental validation of the results in real-world conditions. This research provides a scientific foundation for integrating advanced optimization algorithms into the sustainable design of building materials.