Intelligent Interior Design Systems: Optimizing Layouts and Aesthetics Using Al and User Data

This paper introduces an advanced intelligent interior design system that synergizes deep learning, mathematical optimization, and user data integration to automate and personalize layout generation. Interior design traditionally involves significant manual effort, subjective aesthetic evaluation, and inefficient space utilization. To address these challenges, we mathematically formulated the layout generation problem as a constrained multi-objective optimization task, balancing aesthetics, functionality, and user preferences. Our computational framework leverages convolutional neural networks (CNNs) for layout parsing, graph neural networks (GNNs) for modeling spatial relationships, and Transformer-based architectures for context-aware reasoning. Optimization algorithms, including hybrid evolutionary strategies, are guided by learned aesthetic and functionality scores to efficiently explore the solution space. User data is integrated through latent embeddings and continual learning mechanisms, enabling dynamic personalization and feedback adaptation. Experimental evaluations on both synthetic and real-world datasets demonstrate superior performance compared to rule-based, pure ML, and pure optimization approaches, with notable improvements in layout efficiency, aesthetic quality, and user satisfaction. The proposed system presents significant implications for AI-driven architecture, offering a scalable and adaptive solution for intelligent space design. Future work includes real-time interaction through AR/VR platforms and broader generalization to diverse architectural styles.