Constrained Multi-Criteria Optimization for Integrated Design in Professional Practice

Abstract

To design sustainable architecture, theory encourages architects to rely on automated exploration processes. In practice, the problems encountered are often multicriteria and under constraint. This paper compares different constraint handling strategies, approachable to designer, for processes involving evolutionary algorithms. Four methods are tested on a case study from professional practice. Two methods rely on parametric models: the penalty function method and the use of hyperparameters. The others involve the use of generative techniques: a rule-based method and a repair algorithm that takes the form of an agent-based model. This study highlights the significant impact of the choice of the constraint management method on exploration performance. Among other results, it appears that models involving the use of generative techniques are more efficient than those using parametric models. This calls for the development of dedicated tools.