A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Dynamic and interactive re-formulation of multi-objective optimization problems for conceptual architectural design exploration
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.
Highlights MOO methods mostly focus on the solving rather than reformulation of MOO problems. Optimization problem reformulation (Re-OPF) is key for early design optimization. An innovative MOO method with a dynamic and interactive Re-OPF phase is proposed. The proposed method can lead to quantitatively and qualitatively better solutions. The tool combining Grasshopper and modeFRONTIER can support the proposed method.
Dynamic and interactive re-formulation of multi-objective optimization problems for conceptual architectural design exploration
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.
Highlights MOO methods mostly focus on the solving rather than reformulation of MOO problems. Optimization problem reformulation (Re-OPF) is key for early design optimization. An innovative MOO method with a dynamic and interactive Re-OPF phase is proposed. The proposed method can lead to quantitatively and qualitatively better solutions. The tool combining Grasshopper and modeFRONTIER can support the proposed method.
Dynamic and interactive re-formulation of multi-objective optimization problems for conceptual architectural design exploration
Yang, Ding (author) / Di Stefano, Danilo (author) / Turrin, Michela (author) / Sariyildiz, Sevil (author) / Sun, Yimin (author)
2020-04-29
Article (Journal)
Electronic Resource
English
Simulation-based multi-objective optimization , Conceptual architectural design , Optimization problem re-formulation , Divergent concept generation , Information and knowledge extraction , Self-organizing map , Hierarchical clustering , Hierarchical variable structure , GH-MF integration , Top-daylighting system , DoE , design of experiments , GH , McNeel's Grasshopper , HC , Initial-OPF , Optimization problem initial-formulation , MF , ESTECO's modeFRONTIER , MOO , Multi-objective optimization , OPF , Optimization problem formulation , OPS , Optimization problem solving , Re-OPF , SBMOO , SOM
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