Constraint handling methods for a generative envelope design using genetic algorithms: The case of a highly constrained problem. (September 2022)

• Record Type: Journal Article
• Authors: Duclos-Prévet, Claire; Guéna, François; Efron, Mariano
• Other Names: Gómez Paula Z guest-editor.; Braida Frederico guest-editor.; Lima Fernando guest-editor.; Loyola Mauricio guest-editor.
• Is Part Of: International journal of architectural computing. Volume 20:Number 3(2022)
• Page Start: 587
• Page End: 609
• Publication Date: 2022-09
• Subjects: Optimization under constraint -- performative envelope design -- generative and sustainable design -- agent-based modeling -- multiobjective genetic algorithm -- visual comfort; Architecture -- Data processing -- Periodicals; Architecture -- Informatique -- Périodiques; Virtual reality in architecture -- Periodicals; Computer-aided design -- Periodicals; Architecture -- Data processing; Periodicals; 720.2840285536
• DOI: 10.1177/14780771221120577
• Languages: English
• ISSNs: 1478-0771

Abstract

The use of genetic algorithms as generative and performance design techniques often involves, in practice, constraint handling, which can be a complex task. Moreover, environmental simulations are computationally expensive and managing constraints can avoid wasting time on infeasible solutions. Despite these two incentives, and the benefits of an immense literature, both applied and theorical, on constrained optimization, there are only few guidelines and tools directly applicable by architects to address this issue. This paper proposes to fill this gap by identifying, classifying, and implementing different constraint management techniques available to architects. Seven methods have been tested for a highly constrained envelope design problem, consisting in the optimization of a sun-shading system. Three of them are easily replicable to different types of projects while the four others need to find a problem-specific heuristic. It appears that the second category is more efficient but implies the use of generative techniques that are more difficult to implement than parametric models.