Combined Multi‐Condition Generative Adversarial Network and Force‐Directed Algorithm for Generating Intelligent Residential Layout

ABSTRACT Research on the intricate task of architectural layout design has gained considerable interest. Consequently, diverse automated layout methodologies have been examined. However, most techniques follow rule‐based paradigms, requiring intricate rule customization, which limits their practicality. Accordingly, this study develops an innovative methodology combining the principles of generative adversarial learning with a force‐directed algorithm to acquire, generate, and optimize architectural layout schemes. A dataset for method training is also created. To enhance the model, environmental cues from immediate surroundings are systematically integrated using a multi‐channel methodology. A multi‐scale encoding format enables the model's ability to adapt to various planning conditions, thus improving control. To refine outputs further, post‐processing and adjustment modules for facilitating automatic scheme optimization are integrated. The approach produces outcomes satisfying practical engineering demands. To verify its effectiveness, the method is compared with established methodologies. This breakthrough can assist designers during the initial phases of design, enhancing work efficiency.