[R&D] AI & Architecture: Optimization under Constraints

[R&D] AI & Architecture: Optimization under Constraints

Claire Duclos, researcher at LAB AS Explore at architecturestudio, and François Guéna (MAP-MAAC) explore the integration of constraints in multi-criteria architectural optimization.

Summary:

The article presents the method of repair functions, combining genetic algorithms and generative techniques (cellular automata, agent-based modeling). Four real-world case studies (urban planning, façades, solar protection) illustrate its effectiveness in solving constrained problems.

Key Points:

– Urchin solver (Grasshopper, NSGA-II) for optimization.

– Application to environmental, geometric, and aesthetic constraints.

– Analysis of replacement rates in complex contexts.

Presented at EduBIM 2024, this article showcases our commitment to architectural innovation.

Read the article here.