A Pareto Optimization Problem Formulation for Building Performance and Design

D'cruz, Neville; Radford, Antony; Gero, John S.

doi:10.1080/03052158308960626

Cited by 19 publications

(5 citation statements)

References 8 publications

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“…• The Pareto-based optimisation concept, first introduced in building design in the 1980s by Radford, Gero and D'Cruz [82][83][84][85][86], relies on the identification of a set of all feasible solutions (building design or retrofit options), which is Pareto-optimal or non-dominated (fig 2). Being non-dominated implies that no solution within it can improve an objective without being detrimental to at least another one [87,88].…”

Section: Multi-objective Optimisationmentioning

confidence: 99%

A systematic review of genetic algorithm-based multi-objective optimisation for building retrofitting strategies towards energy efficiency

Costa-Carrapiço

Raslan

González

2020

Energy and Buildings

150

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Most common practices for solving building retrofit problems lack efficiency and overall robustness. Knowledge of novel methods that support decision-making (DM) for retrofitting is critical for sustainability and energy performance improvement. This systematic review for the first time provides a large evidence-base to assess the potential of Multi-objective optimisation (MOO) using Genetic algorithm (GA) for supporting the development of retrofitting strategies and its DM process. From 557 screened studies, 57 were reviewed focusing on outcomes, current trends, and the method's potential, challenges, and limitations. Key findings reveal a strong suitability for solving a wide range of building retrofit MOO problems, based on robust outcomes with significant objectives improvement. However, results also indicate that yielding optimal retrofit solutions may require GA-mixed techniques or modified GA, due to time-consuming and effectiveness issues. Heritage buildings, where qualitative objective function definition is particularly challenging, have been little addressed. Further challenges include: lack of standard systematic approach; complex switch between modelling and optimisation environment; high expertise needed to perform MOO and manage software; and lack of confidence in results. While GA-based MOO's robust evaluation for supporting building retrofit and its DM process needs further research, promising potential is shown overall, when complemented with auxiliary techniques.

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Section: Multi-objective Optimisationmentioning

confidence: 99%

A systematic review of genetic algorithm-based multi-objective optimisation for building retrofitting strategies towards energy efficiency

Costa-Carrapiço

Raslan

González

2020

Energy and Buildings

150

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“…It must be recognized that in the actual optimization problem, there is a certain probability of mismatch between the targets. When a set of uniquely designed concepts are no longer applicable, a more robust multi-criteria optimization decision is needed [59]. C. E. Ochoa et al [60] proposed an acceptance threshold to avoid conflicts between building energy and visual dynamics.…”

Section: Envelopesmentioning

confidence: 99%

“…Changes in the data of reference models for different climate information are usually not recorded or tracked. The schematic diagram and its parameters determined by evidence-based methods are provided as default values to users and other tasks that require rapid simulation [49][50][51]59,66,73]. Obviously, all professional information acquired based on experimental experience, engineering judgment, a series of trials and errors, and catalog information also belongs to the core component of the expert knowledge system [41,50,75,109,114,137,143].…”

Section: Heuristic Knowledge Basementioning

confidence: 99%

A Review of Performance-Oriented Architectural Design and Optimization in the Context of Sustainability: Dividends and Challenges

Liu

Peng

2020

Sustainability

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As most countries have widespread and growing concerns about the sustainable development of society, the requirement to continuously reduce energy consumption poses challenges for the architecture, engineering and construction (AEC) industry. Performance-oriented architectural design and optimization, as a novel design philosophy and comprehensive evolution technology, has been accepted by architects, engineers, and stakeholders for a period of time. Performance in the context of architecture is a widely discussed definition that has long shown a correlation with visual and cultural attributes. Shifting the paradigm of sustainable development while ensuring that the function and aesthetics of the building are not overlooked has been the focus of public attention. Considering the core design elements that affect energy conservation and style performance, the design and optimization of building envelopes, form, and shading systems were selected as research materials. From the perspective of epistemology and methodology, a systematic review of 99 papers was conducted to promulgate the latest development status of energy-efficiency design. This paper manifests a detailed analysis of the design patterns, research features, optimization objectives, and techniques of current approaches. The review found that performance-oriented design optimization can benefit the entire industry from the heuristic knowledge base and the expansion of the design space while maintaining sustainability. In contrast, challenges such as tools, skills, collaboration frameworks, and calibration models are highlighted.

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“…( Bandyopadhyay & Saha 2013). To briefly mention other studies, implementation of pareto optimization to design solutions was introduced in the early 1980s (D'Cruz et al 1983). Although the usage of GAs in multi objective optimization problems has increased in the last decade, the first MOGA was submitted by Schaffer was called Vector Evaluated Genetic Algorithms (VEGA) (Schaffer 1984).…”

Section: Genetic Algorithms In Architectural Designmentioning

confidence: 99%

Proceedings of the 22nd Conference on Computer Aided Architectural Design Research in Asia (CAADRIA)

2017

CAADRIA Proceedings

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A Pareto Optimization Problem Formulation for Building Performance and Design

Cited by 19 publications

References 8 publications

A systematic review of genetic algorithm-based multi-objective optimisation for building retrofitting strategies towards energy efficiency

A systematic review of genetic algorithm-based multi-objective optimisation for building retrofitting strategies towards energy efficiency

A Review of Performance-Oriented Architectural Design and Optimization in the Context of Sustainability: Dividends and Challenges

Proceedings of the 22nd Conference on Computer Aided Architectural Design Research in Asia (CAADRIA)

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