

\chapter{Study of the Art}
\label{ch:Study of the Art}



This chapter serves as a platform to explore the foundational background and the sources of inspiration for this study. We're going to talk about parallel regimes in this chapter, but at least one part from those studies are the inspiration of our approach, which can be regarded as comparisons or supports.
\begin{itemize}
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	\item The background of \glsxtrlong{EP}
	\item The background of \glsxtrlong{GH}
	\item Related works in basic \glsxtrshort{GA} i.g. \textit{Galapagos}
	\item Related works in Octopus, with similar purposes
	\item Related works in advance \glsxtrshort{GA}s i.e.
\end{itemize}

The proposed \glsxtrfull{GH} tool represents a parametric and optimizing \glsxtrfull{GUI} designed for the \glsxtrlong{EP} analysis engine. It distinguishes itself among a plethora of existing tools with similar functionality. The chapter dedicates its efforts to scrutinizing the commonalities and distinctions between these tools and the proposed \glsxtrshort{GH} interface. Moreover, it presents a concise literature review concerning analysis and optimization experiences related to \glsxtrfull{HB} and \glsxtrfull{LBT}. Detailed exploration of other specific facets of the state of the art will unfold in subsequent chapters.

%Then, we disscuss about the anglo-saxon influence in project management in Italy, which indicates that the life cycle concept is rising. And \glsxtrfull{LCC} method is also developing.

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Then, the core process of the \glsxtrfull{NSGA2}\cite{Seshadri_NSGA-II.pdf,10.1007/978-3-642-27207-3_22,996017} is briefly introduced, indicating it has a good ability in studying. In the end the related works in the similar realm are presented.


\section[EP parametric and optimization interfaces]{\glsxtrshort{EP} parametric and optimization interfaces}

\subsection{EnergyPlus platform}

``\glsxtrfull{EP} is an influential open-source, cross-platform whole-building simulation software, originally developed by the U.S. Department of Energy's \glsxtrfull{BTO} in 1997 \cite{EnergyPlus_OfficialSite}. It ranks as one of the most extensively utilized tools for executing performance-oriented energy analyses and conducting optimization processes. \glsxtrshort{EP} employs the \glsxtrfull{IDF} file format, a text-based repository housing all the requisite building data for energy simulations. Each \glsxtrshort{IDF} is composed of an array of meticulously ordered string fields, all aligning with an associated \glsxtrfull{IDD} file linked to a specific \glsxtrshort{EP} release. The \glsxtrshort{IDD} provides an exhaustive catalog of all conceivable \glsxtrshort{EP} objects, along with a specification of the data requirements for each object \cite{bigladder}, elucidating the protocol for \glsxtrshort{EP}in interpreting an \glsxtrshort{IDF} file. Notably, \glsxtrshort{EP} lacks a \glsxtrshort{GUI}, rendering it a challenging platform to navigate, especially for non-experts. This limitation has led to the creation of various tools designed to offer interfaces for \glsxtrshort{EP}, facilitating parametric energy simulations and optimization processes. Parametric interfaces can be categorized into scripting-based and \glsxtrshort{GUI}-based approaches.''\cite{rhino-gh}


\subsection{Interfaces of EnergyPlus}
One notable example of a scripting-based interface is \glsxtrfull{BESOS}. Launched in July 2019, \glsxtrshort{BESOS} stands as a cloud-based, open-source platform rooted in Python code and Jupiter Notebooks, striving to establish a unified interface for traditional modeling tools while harnessing novel optimization and machine learning techniques \cite[p.~5]{EMI_report:_BESOS}. \glsxtrshort{BESOS} is structured as an ensemble of modules, enabling parametric building energy simulations through \glsxtrshort{EP}via Eppy\footnote{``Eppy is a scripting language written in Python for \glsxtrshort{IDF} files and other \glsxtrshort{EP}output files.''\cite[15]{rhino-gh} More on Eppy at https://pypi.org/project/eppy/} or EnergyHub, and executing multi-objective optimizations through the use of evolutionary algorithms via the Platypus library \cite{BESOS_ow}. \glsxtrshort{BESOS} Platform provides a robust avenue for conducting extensive energy analyses, yet it necessitates a certain level of proficiency in coding and familiarity with file formats from the user's end.

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Conversely, graphical interfaces, such as that offered by jEPlus\footnote{JEPlus – An parametric tool for \glsxtrlong{EP} and TRNSYS} \cite{JP_on_Best}, have traditionally been recognized for their user-friendliness, requiring fewer programming skills. jEPlus is an open-source project originally introduced in 2009, implemented in Java. It provides a parametric \glsxtrshort{GUI} for EP, empowering users to define b parameters, edit models, manage simulation runs, and collect results \cite{JP_on_Best}. When paired with an evolutionary algorithm, jEPlus excels in efficiently undertaking building design optimizations, encompassing both single-objective and multi-objective paradigms \cite{Use_jEPlus_as_ebd_ot}. Nevertheless, similar to script-based tools, a fundamental understanding of the "\glsxtrshort{EP} modeling process and the text input files" is a prerequisite for jEPlus users \cite{JP_on_Best}.

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More accessible than jEPlus is the \glsxtrfull{PREDYCE} tool, which is a Python library developed by researchers at \glsxtrshort{POLITO} as part of the \glsxtrshort{EU}-funded project \glsxtrfull{EDYCE} \cite{E-DYCE_D3.2_Free-running-module}. This tool offers a user-friendly interface that allows users to launch (parametric) simulations and select from a range of pre-built actions \cite{en14196429}. Importantly, it doesn't necessitate any prior knowledge of Python and generates highly graphical outputs. \glsxtrshort{PREDYCE} comprises three primary, independent modules (an \glsxtrshort{IDF} editor, Key Performance Indicators calculator, and runner), as well as additional modules (e.g., \glsxtrshort{EPW} compiler) that can be combined to create task-oriented scripts known as "scenarios" \cite{E-DYCE_D3.2_Free-running-module}. The \glsxtrfull{KPI} encompass various aspects, such as thermal comfort, indoor air quality, and ventilative cooling. This tool is primarily designed for free-running building simulations and optimization.

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However, unlike typical \glsxtrshort{GUI}s, it stands out due to its ability to seamlessly integrate with 3D modeling software, notably \glsxtrshort{RH}-\glsxtrshort{GH}. This feature transforms \glsxtrshort{PREDYCE} into a highly graphical interface for \glsxtrshort{EP} and other analysis engines, including Radiance and Daysim. This integration allows users to conduct daylight simulations, encompassing all aspects of comfort when optimizing building design. Furthermore, \glsxtrshort{PREDYCE} offers an intuitive interface with pre-configured Python code, sparing users the need for programming expertise and manual handling of input and output files. These files are automatically edited, read, and translated into numerical values or visual outputs, such as bar charts and annual hourly charts, thereby streamlining the simulation process.


\subsection{A prevailing interface of EnergyPlus: OpenStudio}
\glsxtrfull{OS} \cite{OpenStudio_ow} is a widely used graphical interface for \glsxtrshort{EP}. It operates as a user-friendly \glsxtrfull{SDK} and seamlessly integrates with the SketchUp 3D modeling software. A key feature of \glsxtrshort{OS} is the use of "OS Measures," which are sets of programmatic instructions designed to modify energy models, known as \glsxtrfull{OSM}\cite{OpenStudio_SDK}. For further details, additional references can be found in the \textit{OpenStudio Measure Writer's Reference Guide}\cite{OpenStudio_SDK_G}. These measures facilitate the implementation of \glsxtrfull{ECM} and allow for the creation of building models in a parametric fashion.

This approach, often referred to as "procedural modeling," shares similarities with \glsxtrshort{GH}-\glsxtrshort{HB}, as both methodologies involve linking various components to construct detailed building models. \glsxtrshort{OS} further supports parametric analysis through the \glsxtrshort{OS} \glsxtrfull{PAT}. \glsxtrshort{PAT} systematically applies combinations of \glsxtrfull{OS} Measures to the \glsxtrshort{OSM}, generating and comparing multiple design alternatives to assess their energy performance \cite[p.322~323]{ROTH2016321}. In \glsxtrshort{OS}'s \glsxtrshort{PAT}, users have the choice of specifying measure values. These values can be manually defined, which may be time-consuming, or they can be automatically selected from a predefined range using optimization algorithms. This automated approach facilitates the exploration of broader solution sets \cite{OpenStudio_SDK_PAT}.

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Notably, \glsxtrshort{PAT} closely aligns with the tool presented in this paper, both offering graphical parametric interfaces to \glsxtrshort{EP} and supporting multi-objective optimizations. Furthermore, \glsxtrshort{PAT} can perform daylight analysis through the Radiance engine. However, it's important to highlight that \glsxtrshort{PAT}'s algorithmic optimizations require cloud-based processing, such as Amazon cloud or dedicated servers, due to their complexity. While this approach reduces simulation time, it can be challenging to obtain and manage detailed results, as they are typically large files that need to be downloaded from the server immediately after the simulation, with the risk of data loss if the server is shut down \cite{OpenStudio_SDK_PAT}.

In contrast, the presented tool employs the Wallacei plug-in\cite{WALLACEI,WALLACEI_a}, which can be run locally. While this may lead to longer simulation times, it offers the advantage of periodically saving optimization results during the simulation and storing them within the \glsxtrshort{GH} document. This feature allows for easier analysis of the obtained data, as solutions can be directly reinstated into the \glsxtrshort{GH} environment.


\subsection[DesignBuilder]{DesignBuilder: a parallele tool in addition to EP}
\label{subsec:DesignBuilder}
DesignBuilder \cite{DesignBuildr_ow} is a widely embraced and versatile tool used by architects and engineers. It provides a robust environment for creating diverse building geometries, including complex designs. Notably, DesignBuilder offers seamless interoperability, allowing the import of 3D models created using other \glsxtrshort{BIM}/\glsxtrshort{CAD} tools. ``The DesignBuilder seamlessly integrates various analysis tools, such as \glsxtrshort{EP} and Radiance, providing a holistic view of building performance. Parametric analysis allows for the generation of design curve outputs by adjusting up to two variables \cite{DesignBuildr_ow_Pa}, facilitating quick assessments of design decisions and solution comparisons \cite{DesignBuildr_ow}. DesignBuilder employs a genetic algorithm for optimization, searching for the optimal combination of selected design variables to maximize or minimize specific \glsxtrshort{KPI}s \cite{DesignBuildr_ow_OaPa}. While supporting multi-objective optimization, it is limited to two objectives. In contrast, the presented tool, with the Octopus plug-in, can simultaneously consider a minimum of two and theoretically an unlimited number of objectives \cite{Octopus_plug-in_manual}.''\cite{rhino-gh}


\subsection[conclusion for the platforms]{Conclusion and their weakness}
``Both \glsxtrshort{OS} and DesignBuilder are widely used tools in the field of building performance analysis. ``\glsxtrshort{OS} offers a powerful graphical interface for \glsxtrshort{EP} and enables parametric modeling and analysis. However, it requires cloud-based deployment, limiting its accessibility for local processing. Mentioned in \cref{subsec:DesignBuilder}, DesignBuilder is favored for its versatility and interoperability, allowing the import of 3D models from other \glsxtrshort{BIM}/\glsxtrshort{CAD} tools. Nevertheless, its optimization capabilities are limited to two objectives, which can be restrictive in scenarios that require the consideration of multiple performance goals. Understanding these limitations is crucial when choosing the right tool for a specific project, as they may impact the efficiency and comprehensiveness of building performance analysis.''\cite{rhino-gh}

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To address the limitations posed by cloud-based deployment and the restriction of optimization objectives, a solution lies in finding a platform that enables local execution of \glsxtrshort{EP} simulations while offering the flexibility to deploy advanced algorithms, like \glsxtrshort{NSGA2}.

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In the field of building energy simulation, Octopus, powered by the \glsxtrfull{SPEA2}, has made significant strides (see \cref{subsec:octopus}). Looking at the entire parametric design domain, a new tool has emerged, Wallacei. Unlike Octopus, which incorporates various machine learning components, Wallacei is solely focused on genetic algorithm optimization. It boasts superior visualization capabilities and provides enhanced interactivity for form generation.


Octopus has gained an early advantage in academic research and enjoys widespread popularity. As a result, Wallacei has introduced a specific feature to read and visualize Octopus' outcomes. However, their core algorithmic engines differ, with Octopus using \glsxtrshort{SPEA2} and Wallacei relying on \glsxtrshort{NSGA2}\cite{WALLACEI_a}. Starting from 2020-2021, Wallacei has been increasingly involved in urban design activities, such as urban planning in Barcelona and applications in BIG architecture firm projects\cite{WALLACEI_R}.

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Nevertheless, this integration primarily occurs in design activities. In the realm of academic architectural studies, the \glsxtrshort{NSGA2} hasn't been thoroughly explored yet. The study has shown that \glsxtrshort{SPEA2} and \glsxtrshort{NSGA2} are considered highly similar algorithms with comparable performance\cite{10.1007/978-3-642-27207-3_22}. The former produces better optimization results, while the latter can save nearly half of the simulation time. A summary article highlights that in scenarios with an extensive range of objectives, \glsxtrshort{SPEA2} demonstrates an advantage. However, \glsxtrshort{NSGA2} excels in providing a broader genotype diversity in the final optimized results, making it more inclined to offer a more diverse gene pool as an outcome\cite{KONAK2006992}. And also it produces lots of studies in architecture, see \cref{subsec:wallacei}.


\section[Grasshopper]{\glsxtrshort{GH} linking with \glsxtrlong{EP}} % ok with fontsize=12pt
When considering a platform for conducting comprehensive building performance analysis, \glsxtrshort{GH} emerges as a promising choice. Operating within the \glsxtrshort{RH}-\glsxtrshort{GH} environment, this powerful tool extends its capabilities through various plug-ins. Alongside renowned solutions like Ladybug Legacy and Honeybee, \glsxtrshort{GH} supports other plug-ins that establish a seamless link with \glsxtrshort{EP}, Radiance, and Daysim simulation engines, enabling the execution of parametric performance analyses. One such example is ClimateStudio \cite{Solemma}, a fully parametric software developed by Solemma LLC \cite{Solemma_G}. This advanced tool specializes in daylighting, electric lighting, and conceptual thermal simulations, making it a valuable addition to the \glsxtrshort{GH} ecosystem. ClimateStudio is renowned for its user-friendliness and comprehensibility, much like \glsxtrshort{HB} and \glsxtrshort{LB}, allowing architects and engineers to efficiently perform energy and daylight analyses. Moreover, it offers a range of optimization capabilities, making it a versatile platform for fine-tuning building designs. \glsxtrshort{GH}, in conjunction with these plug-ins, not only streamlines the analysis process but also empowers users to explore design alternatives and assess performance parameters with greater accuracy and flexibility. This comprehensive environment encourages creative solutions and efficient problem-solving in the field of building design and performance optimization.

Repeatedly, to underline, by linking to the \glsxtrshort{EP}, Radiance, our \glsxtrshort{GH} is empowered to simulate the \glsxtrfull{EUI} and \glsxtrshort{DA}.












\section[Related works]{Related works}
The tool developed in this study aligns with a broader framework of research endeavors that integrate parametric modeling with building performance assessment while harnessing the power of \glsxtrshort{GA}s to facilitate fully-automated design optimizations. The mechanics of the algorithm is introduced in \cref{sec: GA in arhitecture design and NSGA-II}. To provide valuable context for this work, we have conducted a comprehensive review of prior studies within this domain. Our focus has been on investigations where the integration of \glsxtrshort{HB} and \glsxtrshort{LB} plug-ins for \glsxtrshort{GH} is complemented by \glsxtrshort{GH} optimization add-ons, including Galapagos, Octopus, and Wallacei.

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\subsection[Galapagos]{Galapagos}
Galapagos, a straightforward \glsxtrshort{GH} plug-in, has historically served as a valuable resource for single-objective optimizations. It leverages an evolutionary algorithm but focuses on addressing a single objective at a time. However, the intricate interplay between optimization objectives, such as daylight performance and energy efficiency, has underscored the need for more advanced multi-objective optimization tools within the \glsxtrshort{GH} environment. Notably, Octopus has emerged as a prominent solution to address this challenge.
%This transition to multi-objective optimization is well exemplified in the work of Qingsong and Fukuda\cite{QINGSONG2016818}. In their research, a simple office building in Beijing was analyzed to identify the optimal window configurations on each of the four cardinal directions. The objective was to maximize useful daylight illuminance (percentage of time illuminance exceeds 300 lx on the analysis plane) while minimizing total thermal energy consumption. Galapagos was employed, with separate processes for daylight and energy optimization. Intriguingly, the optimal window dimensions for daylight and energy goals were found to be distinct. This underscores the limitations of single-objective optimization tools and the additional burden they place on designers to interpret results and strike a balance between often conflicting objectives.


\subsection{Octopus and its practice}
\label{subsec:octopus}
This transition to multi-objective optimization is well exemplified in the work of Qingsong and Fukud\cite{QINGSONG2016818}. In their research, a simple office building in Beijing was analyzed to identify the optimal window configurations on each of the four cardinal directions. The objective was to maximize useful daylight illuminance (percentage of time illuminance exceeds 300 lx on the analysis plane) while minimizing total \glsxtrshort{TEUI}. Galapagos was employed, with separate processes for daylight and energy optimization. Intriguingly, the optimal window dimensions for daylight and energy goals were found to be distinct. This underscores the limitations of single-objective optimization tools and the additional burden they place on designers to interpret results and strike a balance between often conflicting objectives.

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Multi-objective optimization, as exemplified by the Octopus tool, provides a compelling solution to the intricate relationship between daylighting and energy performance, as demonstrated by Toutou et al.\cite{TOUTOU20183595}. In their study, Spatial Daylight Illumination (SDI) and \glsxtrshort{EUI} were the focal points of optimization, considering a set of seven variables encompassing factors like south window-to-wall ratio, window material, wall construction, shading angle and dimensions. This investigation yielded a critical insight: optimal design solutions, when viewed from the perspectives of daylighting and energy, often do not align. This divergence in design objectives is further substantiated in Fang's PhD thesis\cite{Y.Fang.Optimization}. Fang undertook a two-fold optimization process, utilizing the Galapagos plug-in for single-objective optimization. The first optimization sought to maximize Useful Daylight Illuminance (UDLI), while the second aimed to minimize \glsxtrshort{EUI}. Subsequently, an Octopus multi-objective optimization process was employed to identify trade-off solutions that optimize both objectives. Notably, this workflow was applied across varying U.S. climatic conditions, highlighting its versatility and adaptability. Conducting these optimizations under different climatic conditions not only validates the robustness of the established workflow but also underscores its applicability in diverse contexts, as corroborated by previous studies\cite{Passive.p_K.kyle,FANG20197}.

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The utilization of the Octopus plug-in to address conflicting design objectives is well-documented in a range of studies\cite{Zhang2021,ZHANG2017371,PILECHIHA2020114356}. Zhang and Ji , for instance, engaged in multi-objective optimization that simultaneously aimed to minimize cooling energy consumption while maximizing \glsxtrshort{DA} and wind speed\cite{Zhang2021}. This optimization process involved varying \glsxtrshort{WWR} on the four facades of the building under investigation. Another notable example is presented in the work of Zhang A. et al.\cite{ZHANG2017371}, who optimized various design parameters for a school building in China. Their objectives centered on maximizing Useful Daylight Illuminance (UDLI) within a range of 100-2000 lx while concurrently reducing heating and lighting energy requirements and minimizing summer discomfort. The optimization process considered variables such as building orientation, space depth, \glsxtrshort{WWR} of different facades, glazing materials, and shading types. Pilechiha et al. \cite{PILECHIHA2020114356} introduced an innovative approach to assess the "Quality of View" in office buildings while maintaining a balance with energy performance and daylighting. Their study focused on optimizing window location and dimensions through the Octopus plug-in, employing design goals encompassing \glsxtrfull{sDA}, \glsxtrfull{ASE}, \glsxtrshort{EUI}, and Quality of View (QV).

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Multi-objective optimization techniques are frequently employed to identify shading configurations that not only enhance occupants' comfort but also optimize energy consumption. A noteworthy example is the work of Bahdad et al.\cite{BAHDAD2021102996}, who harnessed the power of Octopus to determine the optimal design for light-shelves. Their objective was to strike a balance between minimizing \glsxtrshort{EUI}, maximizing Useful Daylight Illuminance (UDLI), and minimizing \glsxtrfull{DGP}. This use of Octopus for enhancing occupants' comfort and energy efficiency can be observed in various other studies as well\cite{KHIDMAT2022104241,su11247096}. These investigations highlight the versatility of the Octopus tool in addressing a wide range of design objectives and performance parameters, making it a valuable asset for researchers and designers seeking to create sustainable and occupant-centric built environments.

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The study by Bakmohammadi and Noorzai\cite{Bakmohammadi2020Optimization} delves into the complex realm of design optimization, extending beyond the objectives of previous research. In their investigation, a multitude of objectives is considered, encompassing occupants' thermal and visual comfort, as well as energy consumption.


Five distinct goal parameters guide the optimization process:
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    \item Useful Daylight Illuminance between 100 and 2000 lx(UDLI100-2000)
    \item \glsxtrfull{DA}
    \item \glsxtrfull{TEUI}
    \item \glsxtrfull{LEUI}
    \item Occupants’ thermal comfort
\end{itemize}
The integration of an adaptive thermal comfort model, even in conditioned spaces, is indicative of the research's depth. \glsxtrfull{EP} analysis outputs are utilized to calculate \glsxtrshort{TEUI} and \glsxtrshort{LEUI} values, providing a comprehensive perspective. Furthermore, the study acknowledges the importance of glare mitigation by introducing \glsxtrshort{DGP} as a critical measure. This aspect is incorporated into the evaluation process after the optimization phase, allowing for the selection of the final optimal solution.

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Multi-objective optimization extends its applicability to find harmonious trade-off solutions between occupants' comfort, energy consumption, and additional variables. Notably, studies such as Sun et al.\cite{app10072435} introduce the minimization of envelope cost as one of the optimization objectives. In a similar vein, research by Manni et al.\cite{en13184981} explores multi-objective optimization with the aim of balancing comfort and energy efficiency with environmental considerations.

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A comprehensive review of relevant literature reveals that Octopus optimization has predominantly been applied to fully conditioned buildings. In these scenarios, energy use intensity (total \glsxtrshort{EUI} or thermal and lighting separately) is typically included among the optimization objectives, aiming for their reduction. Surprisingly, this method has rarely been utilized to discover optimal design configurations for free-running buildings. In contrast, the proposed tool expands the horizons of optimization by considering thermal and visual comfort metrics without the involvement of mechanical systems. The primary objective is to minimize discomfort by harnessing the inherent potential of the building's design itself. Only in a subsequent phase, the approach introduces \glsxtrfull{HVAC} and lighting systems into the optimization process, suggesting control schedules to eradicate discomfort while concurrently reducing energy consumption. ``Consistent with findings from reviewed papers, the developed workflow optimizes a diverse array of variables, encompassing various design aspects. These include window positioning and configuration, insulation, shadings, and natural ventilation, all directed toward enhancing both visual and thermal comfort.''\cite{Y.Fang.Optimization,Passive.p_K.kyle,FANG20197} As observed in examples, the tool's adaptability is rigorously tested across various real climate conditions, confirming its robustness and versatility in diverse real-world scenarios.


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Manni and Nicolini's insights, as highlighted in \cite{en15020486}, shed light on a critical yet often overlooked aspect of research. Few studies incorporate climate change effects into their multi-objective optimizations, despite the potential for future climatic conditions to render current optimal design solutions obsolete. To address this significant concern, the developed tool is subjected to rigorous testing under various European climates, ensuring its adaptability to diverse environmental conditions. Furthermore, the tool's resilience is put to the test by examining its performance under simulated 2050 weather conditions.

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Eleonora Vignani\cite{rhino-gh} delves further into her research to explore the conditions of the year 2050. She conducted two algorithmic experiments with a focus on comfort, optimizing for visual aspects (max \glsxtrshort{DA}, max \glsxtrshort{UDLI}, min \glsxtrshort{ASE}) and thermal sensation (Max percent of time comfortable). The genes to be optimized include Orientation, four orientations of \glsxtrshort{WWR}, Insulation thickness, \% of glazed area operable for natural ventilation, and shading devices angle.
Here is the list for the objectives:
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	\item Useful Daylight Illuminance between 100 and 2000 lx(UDLI100-2000)
	\item MAX: \glsxtrfull{DA}
	\item MAX: \glsxtrfull{UDLI}
	\item MAX: Percentage of time comfortable
	\item MIN: \glsxtrfull{ASE}

\end{itemize}

And the parameters need to be optimized:
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	\item Orientation
	\item four \glsxtrshort{WWR}s of each orientation.
	\item Insulation thickness
	\item percentage of glazed area operable for natural ventilation
	\item shading devices angle
\end{itemize}

She selected ten European cities and estimated their 2050 weather conditions based on the report for scenarios-1\cite{101}. Subsequently, using the algorithm, she conducted optimization under both current and future climate conditions for each city. Following this, her second experiment applied the algorithm to two specific building renovation projects in the Piemonte region. Her research is extensive in both temporal and spatial scales, highlighting the robustness of \glsxtrshort{GA}.

This not only underscores the significance of practicing Scripting Architecture in the design approach but also emphasizes the need for forward-thinking regarding the raw input data in research. In the design development process, sustainability, climate change, and optimization algorithms can be considered simultaneously. When exploring the future, climate reports, in addition to algorithms, require significant attention.


\subsection{NSGA-II and Wallacei in architecture}
\label{subsec:wallacei}
S. Chaturvedi et al.\cite{buildings10050088} propose a multi-objective optimization framework that integrates \glsxtrshort{BIM} with \glsxtrshort{NSGA2} to optimize building envelope parameters for energy efficiency and thermal comfort. The paper utilizes DesignBuilder software to simulate the energy performance and thermal comfort of a sample office building in Tehran, Iran. Four building envelope parameters are considered: \glsxtrshort{WWR}, window orientation, glazing type, and shading device.
The method is to define two objective functions: minimizing the annual total energy consumption (ATEC) and maximizing the percentage of occupied hours within the thermal comfort range (POHC). \glsxtrshort{NSGA2} is applied to generate a set of optimal solutions (Pareto front\footnote{The introduction and definition of Pareto front can be seen in \cref{subsec:Selection criteria}}) for the two objectives. The outcomes can be summarized as the Pareto front and the optimal values of the building envelope parameters for different scenarios.
The paper analyzes the trade-off between the two objectives and the sensitivity of the results to variations in the parameters. It is found that \glsxtrshort{WWR} and window orientation have the most significant impact on the energy performance and thermal comfort of the building. Furthermore, the paper compares the results with the conventional design method and demonstrates that the proposed framework can achieve better energy savings and higher thermal comfort levels.


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Yang et al.\cite{YANG20171255} emphasize the significance of energy-efficient building design for environmental sustainability, addressing the challenges in balancing conflicting parameters. They highlight the use of \glsxtrshort{GA} to optimize urban building designs. The limitations of existing simulation models are discussed, leading to the development of ENVLOAD for assessing building envelope energy performance. ``The study integrated \glsxtrshort{NSGA2} with a building envelope energy estimation model (BEM) to create a multiobjective optimal BEM decision support system (MOPBEM) for designing green building envelopes. The developed BEM was derived from the ENVLOAD, and the MOPBEM was validated in a real building design case.''\cite{YANG20171255  

Designing green buildings involves multiple parameters like insulation, window area, climate, and orientation, making it a complex task. Subjective approaches often fall short, necessitating optimization. The paper integrate \glsxtrshort{NSGA2} with an energy performance model to achieve:
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    \item minimizing ENVOCOST - the envelope construction cost
    \item minimizing ENVLOAD - the building energy demand 
    \item maximizing WOPR - the façade window opening rate 
\end{itemize}
The optimizing objectives are really similar to ours. But they focus more on the envelope.
In summary, Yang et al. underscore the importance of energy-efficient building design, address challenges, and introduce an optimization model to achieve optimal green building envelopes.


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Similar to Yang's study. Wang et al.\cite{WANG2021102048}, conducted an optimization experiment on an architectural model, illustrating the relationship between traditional \glsxtrfull{GA} and quantum evolution generation (QGA). ``According to the comparison, it can be found that compared with the genetic algorithm, when the quantum genetic algorithm is used to deal with the optimization problem of building energy saving, the ENVCOST will be lower by selecting the appropriate material type and reasonably allocating the area occupied by each part (windows, walls and glass curtain wall) under the condition of ENVLOAD; the rate of convergence is faster; the total window area is larger by 13.8\%, which means that the natural ventilation is better; glass curtain wall ratio increased by 14\%, which means that indoor lighting is better; the generation number to achieve convergence is reduced by 50\%, which means the convergence is faster. And compared with original design, the total cost reduces by 35.3\%.''\cite{WANG2021102048}

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The article addresses uncertainties and the algorithm's robustness, acknowledging that results vary with input parameter changes. Nevertheless, it asserts the algorithm's applicability to building designs in tropical and subtropical regions, providing a valuable contribution to the field.









