An Enhanced NSGA-II Algorithm with Parameter Categorization for Computational-Efficient Multi-Objective Optimization of Active Glass Curtain Wall Shading Systems

To address the limitations of the Non-Dominated Sorting Genetic Algorithm (NSGA-II) in optimizing active glass curtain wall shading systems—particularly its suboptimal convergence efficiency and high computational demands—this study proposes an improved NSGA-II algorithm incorporating parameter categorization. Shading system parameters (e.g., slat width, angle, separation, and blind-to-glass distance) are classified into distinct categories based on their character and optimized sequentially. This phased approach reduces the search space dimensionality, lowering computational complexity while maintaining optimization accuracy. The framework integrates user preferences and climatic adaptability to balance energy efficiency and glare mitigation. The louver parameters were optimized under the same experimental conditions, and the enhanced algorithm exhibits 49% lower energy consumption values and 5% smaller visual discomfort time duration compared to the baseline algorithm in the optimization outcomes.