Introduction to Architectural Design Optimization

Wortmann, Thomas; Nannicini, Giacomo

doi:10.1007/978-3-319-65338-9_14

Cited by 23 publications

(17 citation statements)

References 56 publications

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“…Diversity plays a key role in revealing tradeoffs and compromises characterizing the design problem. Optimization results consisting of a selection of legitimate high-performing design solutions are also preferred by architects (Cichocka et al ., 2017; Wortmann and Nannicini, 2017). However, the exploitative nature of standard EAs and certain commonly used optimization algorithms means that such search processes often produce a population of design solutions that are all very similar, covering only a small area of the design space (Rodrigues et al ., 2013).…”

Section: Introductionmentioning

confidence: 99%

SSIEA: a hybrid evolutionary algorithm for supporting conceptual architectural design

Wang

Janssen

2020

AIEDAM

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Significant research has been undertaken focusing on the application of evolutionary algorithms for design exploration at conceptual design stages. However, standard evolutionary algorithms are typically not well-suited to supporting such optimization-based design exploration due to the lack of design diversity in the optimization result and the poor search efficiency in discovering high-performing design solutions. In order to address the two weaknesses, this paper proposes a hybrid evolutionary algorithm, called steady-stage island evolutionary algorithm (SSIEA). The implementation of SSIEA integrates an island model approach and a steady-state replacement strategy with an evolutionary algorithm. The combination aims to produce optimization results with rich design diversity while achieving significant fitness progress in a reasonable amount of time. Moreover, the use of the island model approach allows for an implicit clustering of the design population during the optimization process, which helps architects explore different alternative design directions. The performance of SSIEA is compared against other optimization algorithms using two case studies. The result shows that, in contrast to the other algorithms, SSIEA is capable of achieving a good compromise between design diversity and search efficiency. The case studies also demonstrate how SSIEA can support conceptual design exploration. For architects, the optimization results with diverse and high-performing solutions stimulate richer reflection and ideation, rendering SSIEA a helpful tool for conceptual design exploration.

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Section: Introductionmentioning

confidence: 99%

SSIEA: a hybrid evolutionary algorithm for supporting conceptual architectural design

Wang

Janssen

2020

AIEDAM

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“…For the algorithm focusing on design exploration rather than providing direct solutions, we acknowledge that the building massing found by the case-study optimization may not satisfy all aspects of architectural design [16,45]. Instead, in the data-poor situation of conceptual design stages, the solution retrieved from the optimization plays a role as the "medium of reflection" in design processes and allows architects to draw inspiration from its result [13].…”

Section: Discussionmentioning

confidence: 99%

“…Parametric models describe design by explicit rules and parameters [16]. Often, the rules encoded are rigorous but inflexible.…”

Section: Parametric Modelling For Building Massing Designmentioning

confidence: 99%

Subtractive Building Massing for Performance-Based Architectural Design Exploration: A Case Study of Daylighting Optimization

et al. 2019

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For sustainable building design, performance-based optimization incorporating parametric modelling and evolutionary optimization can allow architects to leverage building massing design to improve energy performance. However, two key challenges make such applications of performance-based optimization difficult in practice. First, due to the parametric modelling approaches, the topological variability in the building massing variants is often very limited. This, in turn, limits the scope for the optimization process to discover high-performing solutions. Second, for architects, the process of creating parametric models capable of generating the necessary topological variability is complex and time-consuming, thereby significantly disrupting the design processes. To address these two challenges, this paper presents a parametric massing algorithm based on the subtractive form generation principle. The algorithm can generate diverse building massings with significant topological variability by removing different parts from a predefined volume. Additionally, the algorithm can be applied to different building massing design scenarios without additional parametric modelling being required. Hence, using the algorithm can help architects achieve an explorative performance-based optimization for building massing design while streamlining the overall design process. Two case studies of daylighting performance optimizations are presented, which demonstrate that the algorithm can enhance the exploration of the potential in building massing design for energy performance improvements.

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“…e Pareto frontier can be solved using the strength Pareto evolutionary algorithm (SPEA) proposed by Zitzler [23]. A method of finding the Pareto optimal solution set was proposed by Wortmann et al for use in architectural design [24]. Cui et al established a microclimate optimization framework for the optimal design of an urban outdoor space in the cold regions in China [25].…”

Section: Introductionmentioning

confidence: 99%

A Sustainable Design Strategy Based on Building Morphology to Improve the Microclimate of University Campuses in Cold Regions of China Using an Optimization Algorithm

Chen

Cui

Mei

2021

Mathematical Problems in Engineering

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The microclimate affects the quality and efficiency of outdoor spaces of campuses, especially in the cold regions of China. In this paper, we propose a multiobjective optimization method to improve the thermal comfort of the outdoor environment of university campuses in severe cold regions. We used morphology data from 41 universities in the cold region of China to create a layout prototype of a campus cluster. Multiobjective optimization was used, and the effects of sunlight, solar radiation, and wind on the outdoor thermal comfort in winter were considered. A parameterized platform was established for the multiobjective optimization of the microclimate of the simplified model of the campus. A multiobjective optimization based on an evolutionary algorithm was used to obtain 108 groups of nondominated solutions. The optimum outdoor microclimate of the campus was obtained at a building density of 0.21–0.23, a plot ratio of 1.51–1.88, and a road width of 11–14 m. We recommend that buildings are designed based on the wind direction in winter and that the space between buildings is increased.

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Introduction to Architectural Design Optimization

Cited by 23 publications

References 56 publications

SSIEA: a hybrid evolutionary algorithm for supporting conceptual architectural design

SSIEA: a hybrid evolutionary algorithm for supporting conceptual architectural design

Subtractive Building Massing for Performance-Based Architectural Design Exploration: A Case Study of Daylighting Optimization

A Sustainable Design Strategy Based on Building Morphology to Improve the Microclimate of University Campuses in Cold Regions of China Using an Optimization Algorithm

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