Luminous condition of urban canyons; an evolutionary computing-based approach to simultaneously improve outdoor illuminance and indoor visual comfort

doi:10.21203/rs.3.rs-3238797/v1

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Luminous condition of urban canyons; an evolutionary computing-based approach to simultaneously improve outdoor illuminance and indoor visual comfort

https://doi.org/10.21203/rs.3.rs-3238797/v1

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Due to the rapid population growth, urban densification is inevitable. As cities become denser, the environmental quality of urban canyons reduces, and thus the associated energy use of buildings increases. Currently, cities are responsible for 70% of the world’s energy consumption. One of the efficient solutions to address this issue is allowing more solar access into interiors and thus making the most of daylight and solar heat gain. Accordingly, this study aims to develop general daylighting guidelines by optimizing urban and building design factors. This ultimately allows more daylight penetration into urban canyons (vertical daylight illuminance (VDI)) and, subsequently, improves indoor visual comfort (useful horizontal illuminance level (HIL)). This can also reduce the associated lighting and heating (during winter) energy use of buildings. Moreover, the correlation between indoor and outdoor illuminance levels is investigated to bridge the gap between daylight requirements in urban planning and building scale design. In this regard, a multi-objective evolutionary algorithm-based assessment of design variables is conducted employing computational simulation. This determines the extent to which each urban morphology can affect daylight access in both indoor and outdoor environments. Accordingly, the optimal range for different design factors is suggested.

Urban daylighting

Urban morphology

evolutionary computing

genetic algorithm

Architecture

Currently, more than 50% of the world’s population resides in urban environments (United Nations, 2011), which will reach 68% by 2050 (United Nations, 2018). According to the UN (2015), cities are responsible for nearly 70% of energy consumption and more than 70% of greenhouse gas emissions in the world (United Nations, 2015). This statistic is situated in a context where most cities are experiencing rapid growth of population and are adopting dense configurations during their urban transition phase. Although a higher density for urban environments is more economical in terms of land use (Sundborg, 2018), it can reduce solar access due to the shadow cast by the surrounding buildings, resulting in the lack of healthy, energy-efficient living environments (Šprah and Košir, 2020). A contradiction as regards maximizing the use of available space (Dogan and Knutins, 2018) as opposed to performing better with respect to the energy efficiency of the built environment sector surfaces. The building sector accounts for nearly 40% of the total energy demand in Western countries, 24.8% and 11.6% of which is associated with the electricity consumed for lighting purposes in commercial and residential buildings (Energy, 2010). In other words, “buildings are the primary energy-use sector of cities” (Shi et al., 2021), from which an average of 20% is reported to belong to the electricity used for artificial lighting in buildings globally (International Energy Agency, 2016).

Daylighting provides an opportunity for climate change mitigation by improving health levels and reducing CO2 emissions indirectly by cutting the energy demand for lighting (Yu and Su, 2015). Improving daylight availability in indoor spaces brings numerous advantages to its users. Daylighting boosts people's mood (Shishegar and Boubekri, 2016), health, and well-being by balancing circadian rhythms and body (Chen et al., 2019). It can also enhance employees' productivity in office buildings (Bellia et al., 2011). However, very often occupants of buildings located in dense urban environments suffer insufficient daylight access. This is especially true in office interiors (Turan et al., 2020), where people spend most of their time. Currently, 74% office spaces experience insufficient daylight levels in a dense city like Manhattan (Turan et al., 2020). On the other hand, in such dense urban contexts, daylit spaces transact for a higher price (5–6% more) and are considered more economical during their operation period. A substantial financial value is thus associated with daylight and this value should accordingly be considered in project financing and investment phases (Turan et al., 2020). In other words, apartments with sufficient daylight are energy-efficient and thus more valuable to users and investors. These buildings are usually more in demand in markets and have higher lease rates and lower utility costs (Ahmadi, 2019). However, nowadays, even tropical cities that typically enjoyed ample solar radiation are struggling with insufficient solar access due to increasing densification of the urban fabric (Jayaweera et al., 2021).

Several studies have focused on daylight and its associated visual comfort in urban environments employing different methods. A study by Jayaweera et al., in 2021, employed a parametric approach to optimize solar access in terms of both daylight and energy savings in different urban contexts. Their findings indicate that an optimum daylight level (sDA of 75%) can decrease lighting energy demand by up to 12% and 36% for east-west and north-south directions, respectively (Jayaweera et al., 2021). Natanian et al. in 2019 also uses a similar approach (using a Radiance-based tool and daylight autonomy metric) to explore the impact of both building and urban design factors, including typology, window to wall ratio and glazing properties and distance between buildings, floor area ratio, and orientation, on daylight and energy performance of buildings. A considerable performative difference is reported between different designs and densities (Natanian et al., 2019). A comprehensive range of design parameters are also investigated in a recent study by Pan and u (2021), exploring building coverage ratio, floor area ratio (FAR), mean nearest neighbor distance (DOS), mean building height (MBH), vertical uniformity, tree coverage ratio, aspect ratio, sky view factor, total site factor, direct and indirect site factor, canyon axis orientation, and ground surface albedo. Based on the results of this study, an increment of 10% sky view factor (SVF) increases the average daytime horizontal illuminance level up to 71.6%, which accounts for 59.6% when combined with a 1m increase in MBH factor. Nevertheless, DOS has shown a negative effect on daytime site illuminance uniformity (D-SUo). Moreover, sky factor, building height, ground surfaces albedo, and vertical uniformity are found to have the highest impact on outdoor illumination, respectively (Pan and Du, 2021). A multi objective evolutionary algorithm (MOEA) based study of Martins et al. (2014) also considers different design indicators of absolute roughness, porosity, contiguity, FAR, plot ratio, aspect ratio, verticality, number of floors, street width, building setbacks, and their thickness, width, and height to evaluate both irradiance and illuminance levels on the building's facade in a tropical Brazilian climate. Accordingly, design parameters of albedo, aspect ratio, the distance between buildings, building width, and shape factor have the greatest impact on illuminance levels on all building facades facing four main directions (Martins et al., 2014). Generally, it is assumed that randomness; both vertically and horizontally, which refers to the difference in buildings height and the distance between buildings (DBB), has a great influence on the amount of daylight received in urban canyons. Random configurations provide a higher level of useful daylight illuminance (UDI); up to 10.8% compared to uniform ones (Ahmadi, 2019). Nevertheless, the parallel placement of buildings (less horizontal randomness) reduces daylight access and the associated average vertical daylight factor (VDF) compared to shifted patterns. A higher DBB is preferable in daylight-based (Francis and Groleau, 2002), energy-efficient urban design (Chang et al., 2019), and a distance equal to the width of the opposite buildings provides for a sufficient daylight level. This value can decrease for shifted building clusters (Fig. 1, b &c) (De Luca, 2019). Along with the DBB factor, the width of the urban canyon and the street geometry play a crucial role in an urban context. According to Mehjabeen (2020), doubling the streets width improves daylight access by at least 60% in typical high-rise residential dense urban contexts (in Dhaka, Bangladesh) (Mehjabeen, 2020).

In a later study by Natanian and Auer (2020), high-rise buildings are reported to increase Spatial Daylight Autonomy (sDA) as they present lower site coverage and higher DBB (Natanian and Auer, 2020). It is also reported that increasing FAR has the highest impact on VDF, while building typology and their position tend to have a moderate impact on VDF (Šprah and Košir, 2020). A building’s form showed a slight impact of 13.5% on sDA (Mehjabeen, 2020), while shape factor has a direct impact on daylight levels. In other words. a larger building envelope (in relation to their built volume) is more favorable for dense urban environments aiming to improve daylight access (Martins et al., 2014). Building width is also found to have a considerable effect on illuminance level on the west and east fronts of the building, while the average depth of a building is a more important design factor in south and north orientations (Martins et al., 2016). Linear buildings provide a higher level of VDF on their facade compared to point buildings (cubic) in Slovenia (Šprah and Košir, 2020) and Copenhagen, while box-shaped building towers are the most appropriate building typology in Hong Kong. The shape and density of tall buildings have a relatively slight impact on the VDF of their facade. However, when they have the same height as their surrounding buildings, a simpler shape would be more beneficial (higher VSC values) to prevent self-blocking (Li and Tian, 2020). A study by Jung and Yoon suggested that a building’s orientation is an important parameter that has the highest impact on the amount of natural light received in apartment interiors (floor area ratio, building coverage ratio, the distance between buildings, were other design factors studied) (Jung and Yoon, 2018). Increasing the window size of a building’s facade (WWR; from 50–75%), and their material reflectivity (from 30–50%) also increases sDa daylight metric from 51 to 56, and 58 percent, respectively (Ahmadi, 2019). Optimum facade design (WWR, window number, height, and sill height) have also been proven to improve illuminance levels (UDI) and energy use intensity (EUI) by up to 20.56% and 141 kWh/m2/yr in semi-arid areas such as Tabriz, Iran (Shahbazi et al., 2019).

Considering this diversity of design variables at the urban as well as the building scale, this study adopted the use of an evolutionary algorithm-based methodology to investigate and enhance daylight penetration into urban canyons while improving visual comfort in building interiors simultaneously. Optimum configuration showcasing maximum outdoor illuminance (avg. VDI), and indoor useful illuminance (spatial % of UDI 300–3000 lux) are accordingly presented. An additional evaluation of the correlation between VDI_outdoor and HIL_Indoor is also conducted since this correlation has the potential to bridge a significant gap in existing daylight metrics, namely that existing daylight metrics used in exteriors (open spaces) are unable to estimate indoor lighting conditions (inside the buildings). This research thus provides a novel design framework and valuable insights to integrate outdoor daylight access in urban canyons with indoor illuminance levels and ultimately, enhance environmental quality in contemporary cities.

This research involved developing a multi-objective evolutionary problem-solving strategy (Genetic Algorithms) to optimize daylight access in both outdoor and indoor spaces. Evolutionary algorithms (EA) have been used since the early 20th century in different fields to provide optimal solutions based on specified selection objectives (Navarro-Mateu et al., 2018). Among various computational methods employed to find an optimum design solution, the Genetic Algorithm (GA) is by far the most popular evolutionary optimization tool (Nguyen et al., 2014) that has been deployed successfully in multiple studies (Taleb and Musleh, 2015; Xu et al., 2019). In the field of architecture and urbanism, GA’s are typically used to generate variations of urban morphology (given a contextual setting and limitations). A selection process based on the highest performing morphology is subsequently derived from these variations as an optimization process (Caldas and Norford, 2002).

Figure 2 illustrates the workflow employed in this study for examining the correlation between the chosen urban design parameters and three fitness objectives: maximum outdoor daylight access/illuminance (avg. VDI), indoor lighting condition/indoor visual comfort (spatial % of UDI), and maximum density (overall floor count). The correlation between exterior illuminance and interior visual comfort is also investigated.

Each geometrical iteration triggers Honeybee (legacy V.0.0.66) plugin in Rhino Grasshopper, to start both the indoor daylight and outdoor illuminance analysis by Radiance/Daysim simulator engines simultaneously. Ladybug and Honeybee plugins u se validated engines of Radiance (G. Ward, "Radsite: radiance-online.org,”), Daysim (Jakubiec and Reinhart, 2012), and EnergyPlus (Crawley and Pederson, 2001) that have the potential to consider both dynamic and static weather conditions, as well as detailed optical models. Radiance/Daysim is known as the most frequently used software for daylight analysis. Radiance uses a backward ray-tracing technique while a study by Santos et al. states that “Daysim uses the algorithms of Radiance as a foundation to calculate illuminance and luminance profiles from a weather file” (REINHART, 2006; Santos et al., 2017). This simulator engine (Daysim) is also used in different studies to calculate point-in-time metrics such as illuminance level (lux). Finally, the Wallacei V.2.5 tool is used as an analytic engine to trigger a multi-objective EA-based optimization process. The Wallacei plugin, developed in 2018 by Makki et al., uses an evolutionary problem-solving strategy and has the potential to consider multiple fitness objectives. More importantly, this analytic engine provides users with outputs that facilitate and accelerate the analysis process.

Urban models with this evolutionary strategy represent ‘phenotypes’, that contain a combination of different design factors referred to as ‘genomes’ (Fig. 2). Phenotypes are thus governed by a gene pool of design variables that can alter the urban configuration. Variation in urban design factors increases their potential for adaptation to climatic conditions (Navarro-Mateu et al., 2018). In other words, a wider range of design variables allows for forming more extensive configurations, thus increasing the inherent adaptability of the optimal solutions. This objective can be achieved through generative designs, which provide the most advantageous design options according to the set objectives. In this study, the parametrically designed variables include:

Horizontal randomness (density): site coverage (SC), distance between buildings (street width (SW)), floor area ratio (FAR), urban grid rotation (UGR)
Vertical randomness: aspect ratio (AR), mean buildings height (MBH)
Building typology (BT; defined by plot size), Room depth (RD) and Facade design (WWR)

This study tries to reflect daylighting uncertainties in the early stages of design at both building and urban scales. A range of variables are thus selected to cover morphologies offering efficient schemes that consider both indoor (obj.1) and outdoor (obj.2) lighting conditions. Building typologies representing cube and linear (extended in different directions, defined by plot size) models in high-density mode or in other words, high-density and compact geometry is considered as the third fitness objective (obj.3) to investigate daylight penetration in dense urban configurations. For each typology, a detailed evaluation of illuminance level in both outdoor (avg. VDI on facades) and indoor (spatial % of UDI 300-3000lux) built environments is conducted by changing dynamic input parameters, covering all possible combinations of design factors. Table 1 represents the range of design variables incorporating possible urban configurations.

Table 1

Range of design factors (DF) at urban and building scales
Scale	Urban				Building
Variable (DF)	Plot Size in X & Y (m2) (each of 9)	Street Width (m)	Site Coverage	Urban Grid Rotation (°)	Buildings’ Height (m)	Room Depth (m)	Window to Wall Ratio in different orientations (N, W, S, E)
Abbrv.	PT	SW	SC	UGR	BH	RD	WWR
Range	15.00 to 30.00	5.00 to 20.00	0.60 to 0.85	0.00 to 179.00	6.00 to 36.00	2.50 to 3.50	0.30 to 0.90

Design variables form the genomes, and the objectives of maximum density (the overall floor count), and outdoor illuminance (avg. VDI) and indoor visual comfort (spatial % of UDI) are regarded as the fitness criteria. In this study, individuals offering the highest performance are selected as the optimal urban design solutions for cities with similar solar condition to Sydney. Initial modeling settings are presented in Table 2.

Table 2

Model settings
Location		Sydney, NSW, Australia, 33.8688° S, 151.2093° E
Softwares and tools		Rhino V.6.0, Grasshopper, Ladybug & Honeybee (Legacy Versions)
Date and Time		A typical winter day noon (21 July,14:00)
Metrics		Illuminance (lux) (avg. VDI & spatial % of UDI_{300 − 3000lux})
Evaluation Grid		Outdoor: 22 m2, Indoor 11 m2
Streel level	Surface Coverage (SC)	Street	0.20
Streel level	Surface Coverage (SC)	Pedestrians	LRV: 0.4
Building level	Finishing Material	Facade	LRV: 0.35
	Finishing Material	Windows	Double glazing, VLR: 0.75
	Shadings		No interior & exterior shadings

This study uses an approach similar to other studies (Martins et al., 2016; Natanian and Auer, 2020; Šprah and Košir, 2020), and considers a hypothetical model embracing 9 building blocks of different sizes and heights (Fig. 2). The lowest floor of the middle buildings is selected to represent daylight conditions as the worst-case scenarios. The average illuminance level on the facade (Vertical Daylight Illuminance (VDI)) represents outdoor daylight condition while the percentage of useful horizontal illuminance level (300–3000 lux) estimates visual comfort in interior spaces. In this study, an outdoor vertical evaluation grid of 2*2 m2 size and indoor horizontal mesh containing 1*1 m2 cells are considered to reduce the simulation time, as a population size of 2000 determines optimum solutions. The simulation date and time are set to a typical winter day to reflect the worst daylight scenario.

The evolutionary algorithm includes 100 generations, each with a population size of 20 individuals, a mutation probability of 20% (equal to 1/n suggested by Deb et als. (Deb et al., 2000), where n is the number of variables), a crossover rate of 90%, and Elitism size of 50% (fixed by the plugin) are assigned. Mutation probability is “the percentage of mutations taking place in the generation,” while crossover rate represents “the percentage of solutions in the generation that will reproduce in the next generation” (Makki et al., 2019).

2.1. Climatic Context

This study takes the city of Sydney as an example of the urban and climatic challenges and opportunities in a subtropical context. Due to under-exploited daylighting potentials, as well as incremental urban growth, Sydney is an excellent representation of many developing urban areas with a similar condition. According to the New South Wales Department of Planning, Industry and Environment, Australia, Western Sydney is being developed rapidly to become an economic hub. Sustainability is also considered as one of the BASIX policies (NSW government planning measures for sustainability targets) in the strategic plan of Western Sydney development, wherein low-carbon and low-energy infrastructure design is an embedded principle. Despite this, western parts of Sydney is hotter and drier than the inner Sydney, which will increase the overall energy use in this region. Some strategies such as using shadings and high reflectance facade materials are already proposed by the Department of Planning, Industry, and Environment of NSW Government (Department of Planning Industry and Environment, 2019). Nevertheless, by conducting an efficient daylighting schem, the potential lighting and heating (during winter) energy demand of buildings can be reduced, and the concerns of the adverse effects of urban densification in this region can be addressed. Therefore, this study is also seen as having potential to suggest a new approach and guideline for the development of the Western Sydney region while also serving as an example for upcoming urbanizing cities in a similar climatic context.

A successful evolutionary run through Wallacei calculates for 1) Standard deviation value; Equation Ⅰ: \({\sigma }=\sqrt{\frac{1}{N}\sum _{i=1}^{n}{\left(xi-\mu \right)}^{2}}\), and 2) Normal distribution; Equation Ⅱ: \(f\left(x\right)=\frac{1}{\sqrt{2\pi \sigma }}{e}^{-\left(\frac{{\left(x-\mu \right)}^{2}}{{2\sigma }^{2}}\right)},\)per generation.

X: solution’s fitness value

µ: generation’s mean fitness value

σ: standard deviation value

The three presented standard deviation (SD) graphs (Fig. 3, left) are dedicated to the set fitness objectives of maximum indoor visual comfort (spatial % of UDI), outdoor illuminance (avg. VDI), and maximum density (defined by floor count; max 12 floor for each building). The red lines indicate the first generations while blue lines represent the latter ones, while the narrower curves indicate less variation in each objective. This results in a smoother slope in the mean value trendline (Fig. 3, right) demonstrating a higher decrease in average fitness per generation. On the contrary, fitness objectives of indoor and outdoor illuminance comprise higher variations and can be changed by altering urban design factors to a greater extent.

Considering fitness values, the set objectives of the spatial percentage of UDI (300–3000 lux), average VDI on building facade, and density, which is defined by the overall floor count, alter through a range of 42.45–100%, 4.20-31.35 Klux, and 43 to 124 floors, respectively. The domain of each range represents the effective size of the design variables. In other words, the difference between minimum and maximum value achieved in the values of each objective determine the impact of design scenarios on interior daylight sufficiency (obj.2), exterior illuminance level (obj.2), and density (obj.3). Therefore, urban configuration can vary obj.1, obj.2, and obj.3 by up to 57.55%, 86.60%, and 65.32%, respectively. Figure 4 shows the relation between the set fitness criteria covering the entire population, in which a minimum value is desired to achieve the objectives. As shown in Fig. 4, individuals, which indicate to design solutions, are moving towards the minimum limits which means they converged properly and thus are able to provide an acceptable amount of solutions that respond to the set objectives successfully.

To find the correlation between design factors and the fitness objectives, phenotypes and their associated genomes were carefully analyzed. Two methods: 1) Average of fitness ranks (FA) (Equation Ⅲ) RD=(|x₂-x₁|)+(|x₃-x₂|)+(|x₄-x₃|...+(|x_n-x_n−1|), and 2) Relative Difference (RD) between fitness ranks (Equation Ⅳ) \(\text{F}\text{A}=\frac{x1+x2+x3+x4\dots +xn}{n}\), are used to analyze the Pareto front individuals through Parallel Coordinate Plot (PCP) (Fig. 5). The first strategy (FA) provides the possibility to find the extreme individuals that are specialized based on one fitness objective while in the other strategy (RD) individuals tend to find an equilibrium between all fitness objectives (Navarro-Mateu et al., 2018). Accordingly, the best design solutions (top-ranked individuals) for both FA (Gen.69, Indv.7) and RD (Gen.0, Indv.12) are shown in Fig. 5 (black lines).

Among the individuals, solutions with fitness objectives “FO1 ≥ 75%”, “FO2 ≥ 20 Klux”, and “FO3 ≥ 90 floors (a minimum overall of 90 for the whole urban block)” are selected representing the highest performance value. These values tend to repeat several times after the 56th generation. Accordingly, 8 optimum configurations are extracted. The associated genome of each is represented in Table 3.

Individual genes present design factors each with 10 dedicated numerical parameters forming different urban configurations. Based on the findings, a PS between 24–28 m2, MSC of 0.67–0.68, MBH of 26–30 m, MRD of 3 m, UGR of 5°, and a WWR of 0.52–0.64 for the north, 0.42–0.47 for the west, 0.63–0.90 for the south, and 0.44–0.51 for the east facades achieve a high level of illuminance in urban canyons while maintaining visual comfort in interiors.

Comparing these scenarios with the worst cases allows discovering the impact of urban morphology on the set objectives and thus provides more information on inefficient genomes. Table 4 illustrates configurations with maximum fitness objective values, representing minimum performance. Accordingly, UGR, MBH, and building typology (PS (cube or linear)) have the highest impact on the overall values of fitness objectives, while MSC, MRD, and WWR have a smaller effect size. It can thus be concluded that favorable configurations offer a lower MSC, UGR, and a higher MBH, and WWR for each facade. The last solution in Table 4 (Gen.0, Indv.12) represents individuals with almost equivalent fitness ranks concerning all three objectives using the RD method. As shown in Fig. 5 (right), this method represents a horizonal curve (black colored) in the PCP chart, which refers to a similar weight of fitness objectives.

Ultimately, the correlation between outdoor illuminance level and indoor visual comfort shows that an average VDI of 5 to 25 Klux provides a high percentage of UDI 300–3000 lux throughout the room and that lower and higher levels create under lit and over lit spaces. Moreover, there is a strong correlation (R2 = 0.80) between interior visual comfort and outdoor illuminance level. The potential indoor illuminance condition can also be estimated through the equation of the curve in Fig. 6.

This study employs an evolutionary algorithm-based optimization process to derive optimum spatial configurations that increase outdoor illuminance while improving indoor visual comfort simultaneously. This process offers great flexibility and adaptability to the decision making task according to Back, Hummel, and Schwefel (Back et al., 1997), especially when multiple objectives need to be considered. It allows for a wider range of solutions to be generated thus providing more morphological choices. It should also be noted that this pilot study only adhered to an extreme climatic condition based optimization scenario, primarily winter (due to the time required to run the simulation), thus serving as a methodological blueprint for future research. An investigation spanning all seasons (annual) would certainly offer a comprehensive guideline for the set objectives of this study. It is also recommended to utilize a higher resolution evaluation mesh to derive more accurate, higher resolution data on illuminance levels. Exploration of optimal solutions and worst cases derived from this study allows for finding genomes (design factors) that offer either a higher or lower performance based on the set fitness objectives. Moreover, the analysis of the correlation between different objectives also provides the possibility to estimate one objective based on the other objective, which makes the computation process much easier and shorter. For instance, in this study, finding the correlation between indoor visual comfort and outdoor illuminance level on a buildings’ facade (Fig. 6) allows for estimating interior luminous condition based on the average VDI or vise versa.

This study aims to improve daylight conditions in urban contexts with compact morphologies. As population increases, one should bear caution to sustainability and environmental response of built environments, among which solar access and visual comfort are of high importance. This study explores a wide range of design factors at both urban and the building scale via an evolutionary algorithm-based methodology to increase outdoor illuminance while improving indoor visual comfort simultaneously. A set of numerical variables are dedicated to objectives such as ‘plot sizes’, ‘distance between buildings’, ‘urban grid rotation’, ‘building height’, ‘room depth’, and ‘window to wall ratio’ of each façade, thus covering a comprehensive range of urban determinants. Daylight metrics are calculated through an established simulator engine on Radiance/Daysim and the analysis process utilizes Wallacei; an EA-based problem-solving tool. Finally, individuals with the highest performance offering maximum indoor visual comfort and outdoor illuminance level are selected. Accordingly, optimum solutions are represented, and associated genomes are described in detail. The presented methodology can serve as a guideline for developing built environments with a higher daylight performance, thus reducing dissatisfaction within compact dense developments within contemporary cities.

Conflict of Interest

Each of the authors has substantially contributed to conducting the underlying research and confirms no conflict of interest.

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Light Reflectance Value
Visual Light Reflectance
X_n: Solution’s ranking for specific fitness objective

Tables 3-4 are available in the Supplementary Files section.

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Editorial decision: Major revisions
25 Apr, 2024
Reviewers agreed at journal
18 Sep, 2023
Reviewers invited by journal
18 Sep, 2023
Editor assigned by journal
08 Aug, 2023
First submitted to journal
06 Aug, 2023

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Luminous condition of urban canyons; an evolutionary computing-based approach to simultaneously improve outdoor illuminance and indoor visual comfort

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Abstract

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1. Introduction

2. Methodology

2.1. Climatic Context

3. Results

4. Discussion

5. Conclusion

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References

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Supplementary Files

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