Using genetic algorithm to analyse the nanocomposite materials in architectural landscape environment design

This study explores the optimisation of nanocomposite materials in architectural landscape design using a genetic algorithm (GA). Nanocomposites, known for high strength, hardness, and thermal conductivity, offer great potential but require precise design tuning. The research establishes a GA-based model by defining design objectives and parameter space, then develops a fitness function combining user satisfaction and material performance. Through selection, crossover, and mutation, GA generates and refines design solutions. Comparative experiments with artificial neural networks (ANN), ant colony optimisation (ACO), and particle swarm optimisation (PSO) demonstrate GA's superiority; it achieves a tensile strength of 486.5 MPa, compressive strength of 756.3 MPa, and user satisfaction of 8.2 points – outperforming other methods. Results indicate that GA effectively optimises nanocomposite properties to meet diverse landscape design requirements. This work provides a robust framework for intelligent material design, supporting future advancements in sustainable and high-performance architectural environments.