Dynamic and interactive re-formulation of multi-objective optimization problems for conceptual architectural design exploration

Journal article (2020)

Authors

D. Yang South China University of Technology, Design Informatics - Architecture and the Built Environment

Danilo Di Stefano ESTECO SpA

M. Turrin Design Informatics - Architecture and the Built Environment

I.S. Sariyildiz Design Informatics - Architecture and the Built Environment

Yimin Sun South China University of Technology

Research Group

Design Informatics (Architecture and the Built Environment) (TU Delft)

DOI: https://doi.org/10.1016/j.autcon.2020.103251

Hierarchical clustering Optimization problem re-formulation Self-organizing map Conceptual architectural design Divergent concept generation GH-MF integration Hierarchical variable structure Information and knowledge extraction Simulation-based multi-objective optimization Top-daylighting system

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Published Date

2020

Language

English

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Faculty

Architecture and the Built Environment

Department

Architectural Engineering +Technology

Research Group

Design Informatics

Abstract

Simulation-Based Multi-Objective Optimization (SBMOO) methods are being increasingly used in conceptual architectural design. They mostly focus on the solving, rather than the re-formulation, of a Multi-Objective Optimization (MOO) problem. However, Optimization Problem Re-Formulation (Re-OPF) is necessary for treating ill-defined conceptual architectural design as an iterative exploration process. The paper proposes an innovative SBMOO method which builds in a dynamic and interactive Re-OPF phase. This Re-OPF phase, as the main novelty of the proposed method, aims at achieving a realistic MOO model (i.e., a parametric geometry-simulation model which includes important objectives, constraints, and design variables). The proposed method is applied to the conceptual design of a top-daylighting system, focusing on divergent concept generation. The integration of software tools Grasshopper and modeFRONTIER is adopted to support this application. The main finding from this application is that the proposed method can help to achieve quantitatively better and qualitatively more diverse Pareto solutions.

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