Project

An Investigation into the Design Search Space of Parametric Optimisation for Architectural Design Problems

Goal: The aim of this project is to understand how the design search space can affect the result of parametric design optimisation for architecture, especially for the design problems related to building performance. To be more specific, how the shape (width, depth, and dense) of the design search space will pose difficulties and challenges for the parametric design optimisation process to find high-performing solutions, and can we modify the shape of the design search space to facilitate the parametric design optimisation process to produce more useful results.

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Project log

Likai Wang
added a research item
This paper presents a study investigating the impact of design spaces on performance-based design optimization and attempts to demonstrate the relationship between these two factors through the lens of the span of design spaces. The study defines the span of design spaces as the variety of different types of building design that can be embodied by the parametric model; thus, the wider the span, the more likely is the optimization to identify promising types of building design. In order to reveal the relationship between the span of design spaces and performance-based design optimization, the study present a case study that includes design spaces with various spans within a building design optimization problem considering daylighting performance. The result shows that the difference in span can result in significant changes in optimization in relation to fitness and architectural implications.
Likai Wang
added an update
When teaching #EvoMass, a frequently asked question from users and myself is how to set up the parameters for building design generation. These parameters not only define the architectural features appearing in the generated design but also change the design space for optimization search. Thus, I conducted a study with a series of trial optimization runs for the same design problem using different design parameters and investigated how these parameters can change the result in regard to fitness and architectural implications related to building performance. With this opportunity, I also want to discuss the characteristics of design spaces and try to use depth and span to differentiate two types of design spaces. The two dimensions can be important especially for the increasing application of performance-based design optimization to early-stage design exploration and to understand how design optimization can be integrated into designers' divergent exploration and convergent exploitation. The paper for this study is accepted by this year's #caadfutures with the title of "Understanding the Span of Design Spaces - And Its Implication for Performance-Based Design Optimization", and I will be presenting this study at "CF21 Paper Presentations 6" on July 18 (http://www.caadfutures2021.org/#calendar) Lastly, I would like to express my gratitude to the reviewers for their valuable feedback on this paper : )
 
Likai Wang
added an update
The poster we presented on ICCC2019 @ UNC Charlotte for the paper entitled Reshaping Design Search Spaces for Efficient Computational Design Optimization in Architecture.
 
Likai Wang
added 4 research items
Evolutionary design allows complex design search spaces to be explored, potentially leading to the discovery of novel design alternatives. As generative models have becomemore complex, constraint handling has been found to be an effective approach to limit the size of the search space. However, constraint handling can significantly affect the overall utility of evolutionary design. This paper investigates the utility of evolutionary design under different constraint handling strategies. The utility is divided into three major factors: search efficiency, program complexity, and design novelty. To analyze these factors systematically, a series of generative models are constructed, and populations of designs are evolved. The utility factors are then analyzed and compared for each of the generative models
This paper focuses on the use of using appropriate parametric modelling approaches for computational design optimization in architecture. In many cases, architects do not apply appropriate parametric modelling approaches to describe their design concepts, and as a result , the design search space defined by the parametric model can be problematic. This can further make it difficult for the computational optimization process to produce optimized designs. As a result, the design search space needs to be reshaped in order to allow the computational design optimization process to fully exploit the potential of the design concept on improving the design quality. In this paper, we identify two common types of inappropriate modelling approaches. The first one is related to the design search space that lacks proper constraints, and the second is related to the design search space fixed by the conventional design knowledge. Two case studies are presented to exemplify these two types of inappropriate parametric modelling approaches and demonstrate how these approaches can undermine the utility of computational design optimization .
This paper investigates the impacts of constraint handling on the evolutionary designs in terms of time efficiency and evolutionary effectiveness. To analyse this issue systematically, three generative models with different constraint handling strategies were constructed. The locality of the models and the associated positive and negative impacts on evolutionary designs were analysed.
Likai Wang
added a project goal
The aim of this project is to understand how the design search space can affect the result of parametric design optimisation for architecture, especially for the design problems related to building performance. To be more specific, how the shape (width, depth, and dense) of the design search space will pose difficulties and challenges for the parametric design optimisation process to find high-performing solutions, and can we modify the shape of the design search space to facilitate the parametric design optimisation process to produce more useful results.