A Machine Learning-Based Multi-Objective Optimization and Decision Support Framework for Age-Friendly Outdoor Activity Spaces

Thermal comfort and adequate sunlight exposure are essential for maintaining the health of older adults. Although multi-objective optimization (MOO) has been increasingly applied to improve environmental performance in spatial design, most existing studies still rely on computationally expensive physical simulations, and their optimization results often lack interpretability and operability in early design decision-making. To address these issues, this study proposes a collaborative optimization framework that integrates machine learning surrogate models with neural visualization tools to support performance-driven design of age-friendly outdoor spaces at the early stage. Based on survey data from 46 typical Beijing communities, we constructed a parametric model with three objectives: minimizing summer UTCI, maximizing winter UTCI, and maximizing sunlight duration. An XGBoost model is adopted as a surrogate to accelerate performance prediction, while a self-organizing map (SOM) was applied to cluster and visualize Pareto-optimal solutions. The results indicate that the surrogate model achieves high predictive accuracy and reduces overall computational time by approximately 45% compared with conventional physical simulations. Moreover, the SOM-based visual decision process compresses the high-dimensional solution space and reduces candidate schemes by more than 90%, enabling rapid identification of design solutions that balance environmental performance and spatial morphology. The proposed framework improves both computational efficiency and decision support capacity for performance-oriented spatial design and provides a novel methodological reference for the environmental renewal of age-friendly outdoor spaces.