Multi-objective optimization of school environments to foster nature connectedness using NSGA-III in school design

The integration of nature into school environments has been shown to enhance student well-being and academic performance, fostering a deeper sense of connection to the natural world. However, designing school infrastructure that balances nature exposure, sustainability, and cost-effectiveness remains a challenge. This study addresses the multi-objective optimization of school construction designs to foster nature connectedness using the non-dominated sorting genetic algorithm III (NSGA-III). The optimization objectives include maximizing green space for nature exposure, minimizing construction and maintenance costs, maximizing sustainability in materials and processes, and optimizing space utilization efficiency. Constraints related to budget, space, and environmental regulations are also incorporated. By applying NSGA-III, this research generates Pareto-optimal solutions that offer trade-offs between competing objectives, such as enhancing nature exposure while controlling costs and ensuring sustainability. The study compares these optimized designs with traditional school construction approaches, highlighting the benefits of using multi-objective optimization in creating environmentally conscious, cost-effective educational spaces. The results demonstrate that NSGA-III is an effective tool for optimizing school designs that prioritize nature connectedness while adhering to practical constraints. This research provides valuable insights for construction managers, architects, planners, and policymakers involved in the design and construction of sustainable educational environments.