Morphological Optimization of Low-Density Commercial Streets: A Multi-Objective Study Based on Genetic Algorithm

Abstract

Through their open space layout, rich green configuration and low floor area ratio (FAR), low-density commercial blocks show significant advantages in creating high-quality outdoor thermal comfort (Universal Thermal Climate Index, UTCI) environment, reducing regional energy consumption load (building energy consumption, BEC) potential, providing pleasant public space experience and enhancing environmental resilience, which are different from traditional high-density business models. This study proposes a workflow for morphological design of low-density commercial blocks based on parametric modeling via the Grasshopper platform and the NSGA-II algorithm, which aims to balance environmental benefits (UTCI, BEC) and spatial efficiency (FAR). This study employs EnergyPlus, Wallacei and other relevant tools, along with the NSGA-II algorithm, to perform numerical simulations and multi-objective optimization, thus obtaining the Pareto optimal solution set. It also clarifies the correlation between morphological parameters and target variables. The results show the following: (1) The multi-objective optimization model is effective in optimizing the three objectives for block buildings. When compared to the extreme inferior solution, the optimal solution that is closest to the ideal point brings about a 33.2% reduction in BEC and a 1.3 °C drop in UTCI, while achieving a 102.8% increase in FAR. (2) The impact of design variables varies across the three optimization objectives. Among them, the number of floors of slab buildings has the most significant impact on BEC, UTCI and FAR. (3) There is a significant correlation between urban morphological parameters–energy efficiency correlation index, and BEC, UTCI, and FAR.

1. Introduction

The global urbanization rate escalated from 50% in 2007 to 56% in 2023, with the urban population projected to surge from 3.6 billion to 6.3 billion by 2050 [1], and the large-scale construction activities have led to a sharp rise in energy consumption in the building sector [2]. In this context, China’s “dual-carbon” strategic goal and the promotion of the “healthy building” concept have drawn increasing attention to building energy savings reduction and the creation of a healthy living environment [3,4]. According to the “2023 China Building Energy Consumption and Carbon Emissions Research Report” [5], in 2021, the energy consumption of China’s building operation sector reached 1.15 billion tons of standard coal equivalent (TCE). Among this total, public building energy consumption accounted for 42%, equivalent to 480 million TCE—this underscores both the energy-saving potential and emission reduction pressure in the public building field.

Research shows that urban form is closely related to urban energy consumption and outdoor environmental quality [6,7,8]. At the meso-scale, the design of the planform and spatial layout of urban neighborhoods is a key aspect of sustainable urban development. Therefore, multi-objective optimization of building energy consumption, form, and thermal comfort is emerging with the goal of synergistically increasing building energy efficiency [6], improving indoor and outdoor comfort [9,10] and reducing lifecycle costs and carbon emissions [11]. However, existing research remains primarily concentrated on the scale of individual buildings, and the design variables are dominated by building envelope performance [12], building orientation [13] and indoor HVAC system energy efficiency [14]. For example, Wang et al. [15] optimized the passive house envelope to balance energy consumption, thermal comfort and economic cost, and Luo et al. [13] studied the influence of building envelope and orientation on energy consumption and cost in cold regions. Xu et al. [16] optimized the indoor thermal comfort and lighting of a primary and secondary school in Nanjing. Wu et al. [2] constructed a framework to optimize how parameters such as residential building orientation and envelope structure influence energy consumption, lighting conditions, and thermal comfort in China’s hot-summer and cold-winter zones.

In contrast, the research on multi-objective optimization at the block scale is still insufficient. In fact, urban form exerts a notable influence on energy consumption at the regional level and on outdoor comfort [17,18]. Building layout and form will significantly change the distribution of solar radiation in the neighborhood, which in turn affects building energy consumption and pedestrian thermal comfort [19,20]. In urban morphology research, quantitative analysis is typically conducted using indicators such as urban density (e.g., floor area ratio, building density, open space ratio), urban texture (building shape factor, sky view factor, street aspect ratio) and building types [21]. For example, Xu et al. [22] classified residential blocks in Wuhan and found that the shape coefficient, building density and floor area ratio are the primary factors influencing energy consumption intensity. Taleghani et al. [23] conducted a comparison of three block configuration types in the Netherlands and highlighted the crucial role of the building form factor (i.e., surface-to-volume ratio) in terms of annual energy efficiency and thermal comfort during summer. Wai et al. [24] pointed out that shortening building length and increasing low-level porosity (where a high proportion of low-level space is blocked or filled by building entities) can effectively reduce thermal stress at the pedestrian level. Song et al. [25] found that, regarding building layout, the number of rows has a greater impact on the Physiological Equivalent Temperature (PET) in summer than the number of columns (the “number of rows” denotes the count of buildings arranged in a north–south direction, while the “number of columns” represents the count of buildings aligned along an east–west direction).

It is worth noting that although multi-objective optimization models have been widely applied in building types such as residential, office, and education, their exploration at the level of commercial buildings and neighborhoods is still relatively scarce [26,27,28]. In the process of global urbanization, the development mode of commercial blocks presents different characteristics, and high-density commercial blocks have become a typical form of modern urban core areas [19]. Current urban design and architectural research has also focused on the challenges of high-density environments [29,30], while academic attention to low-density commercial districts remains relatively limited [31]. This is primarily attributed to the early urban renewal models driven by economic benefits, where high-density development is more conducive to maximizing returns [19]. However, as urban development shifts from “incremental expansion” to “stock optimization,” low-density commercial districts (especially historic districts) characterized by human-scale and walkability have regained attention due to their unique spatial and cultural values. The small-scale texture and flexible interfaces of such districts are aligned with ergonomics and behavioral psychology, significantly enhancing pedestrian comfort and safety while promoting the organic integration of commercial and public spaces [32,33]. Their role in carrying historical memories, along with the embedded ecological health benefits and community resilience potential, has gradually made them a research hotspot [34,35]. Against this backdrop, studying the morphology of low-density commercial districts with unique historical and geographical backgrounds is particularly necessary for meeting the needs of urban transformation, enhancing commercial vitality and improving pedestrian accessibility [36,37,38].

Meanwhile, focusing solely on reducing energy consumption can no longer meet the increasingly growing demands for quality of life. Against the backdrop of persistent high temperatures, ensuring favorable outdoor thermal comfort (OTC) has become increasingly urgent [2]. As public spaces with dense pedestrian flow and long activity durations, commercial districts feature the coexistence of high energy consumption characteristics and pedestrians’ high requirements for outdoor thermal comfort, making it crucial to explore the synergistic optimization of BEC and OTC in such environments [20,39]. In addition, the floor area ratio (FAR), as a core indicator of planning control, directly affects neighborhood density, functional layout and economy [39], and has been proven to be one of the key morphological factors influencing regional heating energy consumption [40].

In view of this, this study focuses on low-density commercial districts, selecting total building energy consumption (BEC), floor area ratio (FAR) and Universal Thermal Climate Index (UTCI) as core optimization indicators. These three indicators respectively respond to China’s “dual-carbon” strategy, the requirements for intensive and efficient utilization of national territorial space and the goals of livable environment construction. Addressing the potential trade-off relationships among the three, the present study attempts to conduct a multi-objective optimization research on energy consumption, floor area ratio, and thermal comfort in low-density commercial districts in China’s cold regions. A multi-objective optimization workflow for urban district morphology based on the Grasshopper parametric modeling and NSGA-II algorithm is proposed, with the objectives of minimizing BEC and UTCI, maximizing FAR. Research is conducted with the Fengming Street Historic District in Dalian as a case study. Through collaborative simulation, the optimal design scheme for summer is explored, and the correlation and sensitivity between morphological parameters and optimization objectives are analyzed in depth, aiming to provide theoretical reference and technical support for urban planners and designers.

2. Methods

2.1. Research Framework

This study constructs a four-stage research framework based on parametric modeling and multi-objective optimization (Figure 1): (1) Extraction of basic data and determination of variables. The building types within the study area are reasonably simplified to clarify typical building layout forms. Furthermore, key morphological design variables (such as building density, shape coefficient and orientation) are screened, and an optimization objective system (building energy consumption (BEC), outdoor thermal comfort (UTCI), and floor area ratio (FAR)) is established. (2) Parametric modeling and performance simulation. A parametric model is built using the Grasshopper platform to realize the digital generation and dynamic adjustment of district morphology. EnergyPlus is employed for building energy consumption simulation, Ladybug is used to calculate outdoor thermal comfort (UTCI), and planning indicators such as FAR are automatically extracted via algorithms. (3) Multi-objective optimization. Design variables and objective variables are input into the Wallacei multi-objective optimization engine, with the optimization objectives set as minimizing BEC, maximizing UTCI, and maximizing the district’s FAR. Among them, the optimization parameters for Wallacei are set to their default values. (4) Data analysis and scheme generation. The combinations of morphological parameters, simulation results and Pareto frontier solution sets during the optimization process are recorded. Pearson correlation analysis is applied to examine the correlations between morphological parameters and objective variables, and finally, the optimal design schemes that balance energy efficiency, comfort, and development intensity are extracted.

2.2. Study Area

This study selects the Fengming Street Historic District in Dalian as the empirical research object. As a core port city in Northeast China, Dalian features unique architectural and cultural characteristics. Its urban texture integrates cultural elements from China, Russia, Japan and other countries, forming an architectural landscape where historical traditions coexist with modern styles [36]. The Fengming Street area retains a typical low-rise and low-density street-lane spatial structure, with adaptability to historical space and demonstration value for urban renewal. Meanwhile, Fengming Street stands in sharp contrast to the large-scale buildings to its north; through the renovation of low-density commercial districts, a transitional zone between historical and modern architectural forms can be constructed, verifying the urban design strategy of “coexistence of the old and the new”. The specific study area is illustrated in Figure 2: Fengming Street comprises 9 plots, where Plots 1–5 are scattered residential buildings, Plot 6 is a parking lot, Plots 7–8 are collective apartments, and Plot 9 is a park green space. Among them, Plot 5 (210 m in length and 140 m in width), located in the center of the district, is adjacent to a large commercial complex to the north and a large residential area to the south, serving as a crucial hub space. Therefore, it is selected as the research site.

Residential Block Prototypes

The typological approach to architecture has been extensively used in research on urban block forms across the globe, drawing upon the classification of architectural forms established in previous research [21,41,42,43,44]. This study extracted 10 typical building prototypes as research samples based on the diverse building types in Dalian and the literature review conducted (

Table 1. Basic information of 10 building prototypes.

Code	Extract Buildings	Building Model	Ground Floor Plan	Building Type	Number of Layers	Building Area (m2)
E–1				Enclosed	1–3	2050
E–2				Enclosed	1–3	1700
E–3				Enclosed	1–3	1000
S–1				Slab	4–9	1500
S–2				Slab	4–6	1500
S–3				Slab	4–6	1700
S–4				Slab	4–6	1400
H–1				Slab + point	4–6	1037
P–1				Point	4–6	1200
P–2				Point	1–3	918

). Referring to urban morphology classification methods [21], these prototypes can be classified into four main types: 2 point buildings (P–1: 4–6 floors; P–2: 1–3 floors), 4 slab buildings (S–1: 4–9 floors; S–2/S–3/S–4: 4–6 floors), 1 hybrid building (H-1: 4–6 floors) and 3 enclosed buildings (E–1/E-2/E-3: 1–3 floors). In terms of height control, a three-level gradient system was adopted: 1–3-story buildings (E–1/E–2/E–3, P–2) to maintain the texture of the historic district; 4–6-story buildings (S–2/S–3/S–4, H–1, P–1) to meet urban renewal intensity requirements; and 4–9-story buildings (S–1) to form a coordinated transition with the average height of 32 m in the surrounding commercial area. In terms of functional configuration, the plot is dominated by commercial functions, with a multi-story parking lot set on the north side. This not only resolves the parking conflict in the commercial district but also ensures the economic feasibility of the project. Through the systematic combination of building types and gradient control, this spatial organization strategy achieves multiple goals, including the protection of historical features, the renewal of urban functions, and the improvement in economic benefits.

2.3. Urban Morphological Indicators

This study selects three groups of indicators related to urban morphology, which are categorized into three types [19,21,45]: urban density indicators characterizing development intensity, texture feature indicators reflecting spatial organization and type feature indicators embodying building combination forms, as shown in

Table 2. Indicators related to urban morphology.

Indicator Name	Calculation Formula	Illustration
FAR	$\begin{array}{c}FAR={\displaystyle \frac{S_i}{A_s}}\end{array}$
OSR	$\begin{array}{c}OSR={\displaystyle \frac{A_s-A_f}{S_i}}\end{array}$
BD	$\begin{array}{c}BD={\displaystyle \frac{A_f}{A_s}}\end{array}$
AF	$\begin{array}{c}AF={\displaystyle \frac{S_i}{A_f}}\end{array}$
SF	$\begin{array}{c}SF={\displaystyle \frac{S_a}{V_i}}\end{array}$
SVF	$SVF={\displaystyle \frac{{\Sigma }_{i=1}^n{w}_i\cdot v_i}{2\pi }}$

$S_i$ is the product of the floor area ($A_f$) and number of floors of each building ($F_i$).

.

The first group includes indicators related to urban land use: floor area ratio (FAR), open space ratio (OSR), building density (BD), and average number of floors (AF). The calculation method of FAR is as follows:

$$FAR={\displaystyle \frac{S_i}{A_s}}$$

(1)

where $S_i$ is the total building area (m²) and $A_s$ is the total site area (m²). FAR is a crucial indicator in urban planning, used to measure building density and land use intensity. The calculation method of OSR is as follows:

$$OSR={\displaystyle \frac{A_s-A_f}{S_i}}$$

(2)

where $A_f$ is the building base area of the district (m²). The open space ratio was originally employed to quantify the relationship between open space and the height of building plinths, acting as an indicator to assess the correlation between open space and building scale. It is commonly applied in high-density urban zones or architectural design. The calculation method of BD (specifically referring to building coverage ratio) is as follows:

$$BD={\displaystyle \frac{A_f}{A_s}}$$

(3)

Building density is a key indicator for measuring land use intensity and development density. By reasonably controlling building density, the sustainability and efficiency of urban development can be achieved. In urban planning, architectural design and policy formulation, building density is a critical parameter that needs to be adjusted and optimized according to specific circumstances. The method for calculating the average number of floors (AF) in the district is as follows:

$$AF={\displaystyle \frac{S_i}{A_f}}$$

(4)

This formula is a weighted average method based on total building area and base area, which is applicable to blocks with significant differences in building base areas. Compared with traditional methods, it can better reflect the impact of building height on the overall morphology of the block.

The second group includes indicators related to building geometric shapes: building shape factor (SF) and sky view factor (SVF). Method for calculating the SF is as follows:

$$SF={\displaystyle \frac{S_a}{V_i}}$$

(5)

where $S_a$ is the total external surface area of buildings in the block (m²) and $V_i$ is the total volume of buildings in the block (m³).

SVF is used to measure the proportion of visible sky at a certain point, reflecting the degree of openness of urban space. It affects the microclimate and thermal environment by directly influencing incident solar radiation, with a range of 0–1. A higher SVF indicates a more visible sky and greater received solar radiation. Current SVF calculation methods include geometric methods, hemispherical photography, and numerical simulation. This study uses Grasshopper–Ladybug to calculate the average SVF value of outdoor spaces in the block. The calculation method is as follows:

$$SVF={\displaystyle \frac{{\Sigma }_{i=1}^n{w}_i\cdot v_i}{2\pi }}$$

(6)

where $n$ refers to the count of discrete sampling points distributed on the sky hemisphere (determined by the sampling accuracy set by the user); $w_i$ represents the solid angle corresponding to the i-th sampling point (unit: steradian); $v_i$ denotes the visibility coefficient ($v_i$ = 1 if the i-th sampling point is not obscured by surrounding obstacles, and $v_i$ = 0 if obscured).

The third group comprises building types. This study summarizes four types: point, slab, enclosed and hybrid (as shown in Table 1 above).

2.4. Generation of Land Parcel Model

As shown in Figure 3, this study divides the research area into 6 standard units of 70 m × 70 m using a 3 × 2 grid. This planning scheme is based on multi-dimensional comprehensive considerations. In terms of scale control, the 70 m unit size is compatible with the 50–80 m texture of traditional commercial blocks. Pleasant street spaces (with a D/H ratio of 1.0–1.5) are formed through a 5 m road setback line and a 10 m building setback line. In terms of functional organization, each 4900 m² unit can carry cultural exhibitions, characteristic commerce, or comprehensive services in a differentiated manner. Among them, the multi-story parking lot, as a mandatory element, is allocated in the unit on the north side adjacent to Henglong Plaza to alleviate the current 35% parking gap. At the parametric control level, a control system is established with building types (10 prototypes), spatial positions (coordinates of 6 units) and floor numbers (classified by prototype) as core variables, and a unified 4 m floor height and 5 m road red line as constants. A digital workflow for generating land grids (Ladybug plugin) and automatic building arrangement (Wallacei genetic algorithm) is realized through the Grasshopper platform, ultimately forming a spatial scheme that balances the continuation of historical texture, composite functional development and optimization of traffic organization.

On this basis, we conducted a pre-experiment, setting the ten building prototypes as variables that can appear repeatedly on the site. The experimental results show that with the advancement of the optimization process, the optimal schemes exhibit a significant characteristic of high homogeneity, that is, the same building prototype is selected multiple times. This phenomenon causes the commercial block to gradually approach residential areas in terms of spatial form and functional characteristics, making it difficult to highlight its uniqueness and recognizability as a commercial space. In view of this, the research team adjusted the experimental logic, setting the variable constraint condition as “none of the ten building prototypes are repeated in the site”. The random selection process components are shown in Figure 4.

3. Simulation Process and Optimization Algorithm

3.1. Simulation Tools and Data Processing Tools

This study adopts a multi-software collaborative simulation analysis system, constructing a complete simulation workflow through the parametric modeling platform Grasshopper (a visual programming plugin based on the Rhino 7), the energy consumption simulation engine EnergyPlus 1.8.0 (A publicly accessible building energy simulation tool developed under the auspices of the U.S. Department of Energy (DOE)), and the data processing platform PyCharm Community Edition 2024.3.4. Among them, the geometric models generated by Grasshopper 1.0.0007 can be automatically converted into EnergyPlus energy consumption models, while Python scripts are responsible for extracting simulation results and establishing a mapping database between design parameters and performance indicators, ultimately realizing the full-process automation from parametric design to performance optimization.

3.2. Building Performance Simulation Based on Grasshopper

Three specific optimization indicators are selected for this study: minimizing the total building energy consumption (BEC) of the block, maximizing the floor area ratio (FAR) of the block, and minimizing the Universal Thermal Climate Index (UTCI). The design variables include the number of floors per building, building types and their locations. Grasshopper is used to construct a parametric model for case building, with its plugins applied to simulate the block’s energy use intensity and thermal comfort, as well as calculate the block’s FAR. The schematic diagram of the UTCI calculation method and iterative building location is shown in Figure 5.

BEC quantitatively characterizes the energy usage of buildings, serving as a key metric in building energy efficiency and sustainable development initiatives. It encompasses the total energy demand of a structure, including heating, cooling, equipment, and lighting loads. Building energy simulations are carried out via Honeybee, a tool that depends on the EnergyPlus engine. UWG (Urban Weather Generator) serves as the main urban-scale building and climate simulation modeling tool, used to calculate the temperature inside urban canyons based on measurements from operational weather stations located in open areas outside the city [46]. This research uniformly sets the functional type of buildings in the block to commercial. Building parameters are mainly established in accordance with China’s relevant building codes: GB 50176-2016 [47]. These parameters include the operation time of building air conditioning and heating systems, indoor air conditioning setpoint temperature, lighting power density and switch-on/off time, floor area per person in the room, hourly occupancy rate, etc. In addition, detailed settings are made for the thermal performance and structural information of building envelopes to ensure that the modeling conforms to international envelope settings (

Table 3. Parameter settings for commercial buildings in EnergyPlus.

Parameter Type	Parameter Name	Settings
Time and Date	Start Date—Time	22 July, 6:00–22:00
Meteorological boundary conditions	-	EPW file modified from UWG’s modeled microclimate results
Building	Floor	U value = 0.5 W/m2·K
	External Wall	U value = 0.8 W/m2·K
	Window	U value = 2.2 W/m2·K (SHGC = 0.35)
	Roof	U value = 0.8 W/m2·K
Window-to-wall ratio	North/East/West Facades	0.4
	South Facade	0.6
Internal Thermal Gain	Lighting	Power Density: 10.0 W/m2
	Electrical Equipment	Power Density: 13 W/m2
	HVAC System	Operating Hours: 8:00–21:00
	people	Floor Area per Person: 8 m2/person
		Fresh Air Volume per Person: 30 m3/(h·person)
HVAC System	Cooling Setpoint Temperature	26 °C
	Heating Setpoint Temperature	18 °C

).

EPW meteorological data files can be applied in multiple fields such as building energy consumption simulation, thermal comfort, and daylighting. They contain 8760 h of meteorological data throughout a year, including parameters like air temperature, relative humidity, wind speed, wind direction and solar radiation. When conducting outdoor thermal comfort simulations at the block scale, using EPW meteorological data directly results in relatively large errors. Therefore, it is necessary to use the Urban Weather Generator (UWG) tool in the Dragonfly plugin for microclimate simulation to improve the accuracy of meteorological data [48]. UWG is mainly divided into building form settings and urban parameter settings, which include the thermal properties of building envelopes, block functions, etc. Default settings are applied to parameters including traffic, subgrade materials and albedo ratio; specifically, the road reflectivity is set to 0.3 and the vegetation reflectivity to 0.35. The thermal parameters align with those used in the energy consumption simulation, and relevant details are presented in Table 3. Meanwhile, after setting the buildings as new commercial buildings for simulation operation, the modified urban meteorological files are output in epw format.

3.3. Multi–Objective Optimization

Multi-objective optimization (MOO) can be mathematically defined as a problem scenario where multiple conflicting objective functions are optimized concurrently. Its core characteristic lies in identifying a set of trade-off solutions (known as Pareto-optimal solutions) rather than pursuing a single optimal outcome. A Pareto-optimal solution, also termed a non-dominated solution, refers to a solution in multi-objective optimization problems in which improving any single objective cannot be achieved without degrading at least one other objective. A problem of multi-objective optimization may be formulated as

$$\left\{\begin{array}{c}min \mathrm{F}\left(\mathrm{x}\right)={\left[f_1\left(x\right),f_2\left(x\right),\dots ,f_k\left(x\right)\right]}^{\mathrm{\top }}\\ \mathrm{s}.\mathrm{t}. g_{i }\left(x\right)\le 0, i=1, 2, ... ,m\\ \begin{array}{c}\mathrm{ }\mathrm{ }\mathrm{ }{h}_{j }\left(x\right)=0, j=1, 2, ... ,p\\ x\mathrm{ }\in \mathcal{X}\subseteq \mathbb{R}\end{array}\end{array}\right.$$

(7)

where x represents the decision variables (e.g., air conditioning temperature settings, equipment capacity), F(x) is the objective function vector (e.g., energy consumption, cost, comfort), and$g_{i }$,$h_j$ denote the constraint conditions (e.g., budget, physical limitations). The feasible solution set (solutions that satisfy all constraints simultaneously) is denoted as S, and the objective space (the mapping of feasible solutions under the objective functions) is Z = f(S). For a Pareto optimal solution $x^{*}\in S$, if and only if$\nexists x\in$ S such that,$\forall i \in \left\{1,\dots ,k\right\},f_i\left(x\right)\le f_i\left(x^{*}\right)$, $\exists j \in \left\{1,\dots ,k\right\},f_j\left(x\right)\le f_j\left(x^{*}\right)$. That is, there is no other solution that is not inferior to $x^{*}$, cross all objectives while being strictly superior in at least one objective. At this point,$x^{*}$ represents the optimal solution to the multi-objective optimization problem. Additionally, the treatment of the consistency problem of objective functions is as follows:

$$\mathrm{M}\mathrm{a}\mathrm{x}\mathrm{ }\mathrm{F}\left(\mathrm{x}\right)⟺ \mathrm{M}\mathrm{i}\mathrm{n}-\mathrm{F}\left(\mathrm{x}\right)$$

(8)

That is, all objectives need to be unified as either minimization or maximization. For example, maximizing “comfort level” is transformed into minimizing “—comfort level”.

Wallacei serves as the multi-objective optimization tool utilized in this research. It is an evolutionary algorithm plugin for Grasshopper. Through the NSGA—II genetic algorithm, it examines the correlation between the specified design variables and optimization indicators [49]. It is specifically developed for multi-objective optimization (MOO) and parametric design exploration, and generates a set of Pareto-optimal solutions through multi-objective optimization processes. The optimization parameters and the configuration of settings for the Wallacei tool are presented in

Table 4. Parameter settings for Wallacei optimization.

Algorithmic Parameter	Parameter Effects	Parameter Settings
Crossover Probability	Controls the probability that two parent individuals will exchange genes (parameters). The higher the value, the more rapid the emergence of new populations, generally taking the value 0.1–0.99.	0.9
Mutation Probability	The probability that an individual undergoes random variation. High probability increases diversity but may deviate from the optimal solution; low probability tends to fall into local optimality.	0.1
Crossover Distribution Index	Smaller values mean that the crossover process children are farther away from the parent, introducing more variation. Larger values mean that the crossover process children are close to the parent, preserving a greater proportion of the parent’s traits.	20
Mutation Distribution Index	A smaller value means that the mutation process offspring are far away from the parent, introducing more variation. Larger values mean that the offspring of the mutation process are close to the parent, retaining more of the parent’s characteristics.	20
Random Seed	The value dictates the manner in which the algorithm undergoes initialization.	1
Population Size	Total number of individuals per generation involved in the evolutionary algorithm.	40
Max Generations	Determines the longest number of generations for which the algorithm will run.	50

.

This research seeks to optimize the building performance of block structures through the optimization of design parameters. Considering the influencing factors of BEC, UTCI and FAR, 15 design parameters, including the floor count of each individual building and building orientation, are selected as variables for subsequent optimization. The design variables’ selection and the ranges of their variation are illustrated in

Table 5. Selection of design variables.

Design Variable	Variable Name	Unit	Value Range	Increment Step
Number of Floors of Building E–1	X1	-	[1, 3]	1
Number of Floors of Building E–2	X2	-	[1, 3]	1
Number of Floors of Building E–3	X3	-	[1, 3]	1
Number of Floors of Building S–1	X4	-	[4, 9]	1
Number of Floors of Building S–2	X5	-	[4, 6]	1
Number of Floors of Building S–3	X6	-	[4, 6]	1
Number of Floors of Building S–4	X7	-	[4, 6]	1
Number of Floors of Building H–1	X8	-	[4, 6]	1
Number of Floors of Building P–1	X9	-	[4, 6]	1
Number of Floors of Building P–2	X10	-	[1, 3]	1
Angle of Building E–1	X11	°	[0, 360]	90
Angle 1 of Building E–3	X12	°	[0, 360]	90
Angle 2 of Building E–3	X13	°	[0, 360]	90
Angle 3 of Building E–3	X14	°	[0, 360]	90
Angle 4 of Building E–3	X15	°	[0, 360]	90

.

4. Results and Discussion

4.1. Results of Multi-Objective Optimization

4.1.1. Spatial Distribution of Multi-Objective Optimization Results

The experiment took a total of 162 h, and finally, 2000 sets of calculation data were obtained. Figure 6 presents the distribution pattern of the design variables, simulation results, and the data distribution of optimization objectives for the 2000 data samples, where S represents the total building area and BS represents the number of building floors.

The BEC of the block ranges from 6000 to 20,000 KWh, mainly concentrating between 9500 and 14,000 kWh. The FAR of the block ranges from 0.5 to 1.8, with the main concentration between 0.9 and 1.3. The UTCI ranges from 21.40 to 21.80 °C, mainly focusing on 21.53–21.65 °C. It can be seen that the BEC and FAR of the block buildings have a wide distribution range, indicating a considerable optimization space.

In this study, a total of 2000 operations were performed, and 1428 sets of feasible solutions were obtained after removing duplicate solutions, among which 247 are Pareto-optimal solutions (non-dominated solutions). In other words, no alternative solution exists that is superior or equivalent across all objectives while being strictly better in at least one. The total design space explored in this study comprises 45,360 cases, with 4.4% of the space being investigated. Among these, the 1428 feasible solutions exhibit a broad distribution range in terms of objective variables. This distribution not only includes extreme combinations such as “low energy consumption—low floor area ratio” and “high energy consumption—high floor area ratio” but also covers intermediate transitional states. Such a distribution effectively reflects the trade-off relationships among the three objectives, thereby providing a sufficient sample basis for identifying Pareto-optimal solutions (247 in total). Figure 7 illustrates the spatial arrangement of the solution set, which comprises all feasible solutions derived from this study. Each dot denotes a feasible solution: gray dots correspond to feasible solutions, and red dots indicate Pareto optimal ones. As observed, the Pareto-optimal solutions lie at the leading edge of the feasible solution set. Multi-objective frontier solutions play a key role in architectural design, especially when dealing with complex and conflicting design objectives, as they can optimize the decision-making process through systematic methods. Additionally, we found that there exists a high collinearity with BEC, which leads to phenomena such as the optimization problem being close to a two-dimensional one. This is because the increase in FAR in the study is primarily achieved by adding building floors. In EnergyPlus simulations, a rise in building height leads to a significant increase in building energy consumption (BEC), which is consistent with the conclusion in Section 4.3 of the sensitivity analysis that “the variable of the number of floors has a relatively high impact on BEC”. During the optimization process, the core trade-off relationship is indeed centered on “FAR-BEC”, forming an approximately two-dimensional optimization space. This characteristic does not weaken the research value; instead, it clearly reveals the core contradiction between “development intensity (FAR) and energy consumption (BEC)” in the renewal of historical districts, providing a direct basis for setting a reasonable FAR threshold in planning.

Figure 8 presents detailed information about solutions that are Pareto-optimal. Among buildings with 1–3 floors (E–1, E–2, E–3, P–2), the 1st floor has the highest occurrence frequency. For buildings with 4–6 floors (S–2, S–3, S–4, H–1, P–1), the 4th floor is the most frequent in S–3, S–4, and H–1; the 5th floor appears most often in H–1 and P–1; while for buildings with 4–9 floors, the 7th floor has the highest frequency. In addition, we found that there are no three–floor schemes for Building E–1, nor six–floor schemes for Building S–3. This may be because during the screening of Pareto-optimal solutions, these schemes performed poorly in terms of BEC or UTCI and were eliminated by dominated solutions due to “compromising other objectives”.

4.1.2. Comparative Analysis of Dominated Solutions and Non-Dominated Solutions

This study plotted boxplots of dominated solutions and Pareto-optimal solutions to compare the central tendency, dispersion, and outliers of the objective functions. A dominated solution can be understood as follows: if solution A performs no worse than another solution B in all optimization objectives and is strictly better than B in one objective at the minimum, then A dominates B. As shown in Figure 9a, the dominated solutions for building energy consumption (kWh) of the block are concentrated between 6813 and 18,328, with a median of 11,876 and an average of 11,902. There are outliers in the dominated solutions that are higher than the upper limit of the boxplot. A wider distribution scope (6213–19,790) is observed for the Pareto-optimal solutions, and their median (11,053) as well as average (11,479) are both lower than the corresponding values of the dominated solutions, indicating that they are overall better in energy consumption control. As illustrated in Figure 9b, the dominated solutions for UTCI are concentrated between 21.43 and 21.75, with the median and average both close to 21.59. The Pareto-optimal solutions are concentrated between 21.41 and 21.76, with a median of 21.55 and an average of 21.57, slightly lower than the dominated solutions, reflecting the optimization effect on the outdoor thermal environment. As illustrated in 9c, the solutions dominated in terms of the floor area ratio (FAR) are concentrated between 0.658 and 1.602, with a median of 1.096 and an average of 1.099. The Pareto-optimal solutions for FAR are concentrated between 0.600 and 1.687, with the median (1.056) and average (1.079) slightly lower than those of the dominated solutions, reflecting the trade-off relationship among the three objectives of BEC, UTCI and FAR.

The study reveals that the Pareto-optimal solutions for all three objective functions exhibit a higher degree of dispersion than the dominated solutions, with a wider IQR (Interquartile Range) for the Pareto-optimal solutions. This could be attributed to the fact that the set of dominated solutions is clustered in locally optimal regions, whereas the Pareto-optimal solutions are scattered across the frontier, encompassing solutions with diverse trade-off directions. Furthermore, it is observed that the median and mean values of both the block BEC and summer outdoor UTCI for the Pareto-optimal solutions have lower values compared to the dominated ones. This indicates that the Pareto solutions generally outperform the dominated solutions in terms of these two optimization objectives. However, the Pareto solutions for the floor area ratio (FAR) are also lower than the dominated solutions. This might stem from the strong conflict between FAR and other objectives: the Pareto frontier may be compelled to sacrifice FAR to achieve superior performance in other objectives. Alternatively, there could be a small number of “low FAR—extremely low energy consumption” solutions among the Pareto solutions, which pull down the overall median and mean. In contrast, due to their concentrated distribution, the dominated solutions have higher median and mean values.

4.1.3. Analysis of Block Performance Improvement

In the research on multi-objective optimization of buildings, evaluating the degree and efficiency of performance improvement in optimization results is a key issue. To compare the improvement in block performance, the ideal point method was adopted by us to identify the sole optimal solution, obtaining the solution with the best BEC (6214 kWh), UTCI (21.413 °C), and FAR (1.687). The ideal point is composed of the optimum values of each objective among all Pareto-optimal solutions. Typically, the ideal point does not lie on the Pareto frontier, as no single solution can simultaneously achieve the optimal value for all objectives. Since the block in this study was randomly generated using parametric tools, there is no initial building reference for comparison. Therefore, we compared the results with the extremely inferior solutions among the generated schemes. Compared with the extremely inferior solution (BEC: 19,790.4 kWh, UTCI: 21.764 °C, FAR: 0.614), the optimal BEC scheme reduced energy consumption by 68.6%, the optimal UTCI scheme reduced thermal comfort index by 1.6%, and the optimal FAR scheme increased the floor area ratio by 174%. Based on the ideal point, the feasible solution with the smallest Euclidean distance to it on the Pareto frontier was identified as the 1581st generation scheme, which yielded the actually existing solution with BEC (13,212.6 kWh), UTCI (21.479 °C) and FAR (1.245) (Figure 8). Compared with the extremely inferior solution, this scheme reduced BEC by 33.2%, decreased UTCI by 1.3% and increased FAR by 102.8%. These results indicate that Wallacei’s optimization algorithm can achieve multi-objective optimization of energy consumption, thermal comfort and floor area ratio for block buildings.

4.2. Trend Analysis of Pareto-Optimal Solutions in the Optimization Process

Thirty block schemes were selected from 247 Pareto-optimal solutions to reflect the development trend of the optimization process. The selection rules are as follows: the 1st to 5th solutions in the optimal solution set, one solution selected every 12 solutions in between, and the 243rd to 247th solutions, forming a total of 30 schemes.

As shown in Figure 10, the numbers indicate the positions of Pareto solutions in the course of optimization, with the corresponding objective values listed under each scheme. In the initial phase of the optimization process, the intervals between the positions where Pareto-optimal solutions appear are relatively large, reflecting that the algorithm conducted extensive exploration initially, attempting to find different non-dominated solutions in the entire feasible solution space. With the iteration of the algorithm, the intervals between Pareto-optimal solutions in the later stage become smaller, indicating that the algorithm has roughly identified the distribution area of better solutions and started to focus on refined searches within these relatively optimal areas. It continuously explores schemes closer to the true optimal solution, reduces the scope of the search and improves Search precision, reflecting the convergence of the algorithm.

In addition, it is found that except for S–3, which appears consistently, E–1 and E–2 (enclosed buildings) always appear simultaneously, with only two instances of their absence among the 247 solutions. This is completely opposite to previous research [21], which found that the occurrence frequency of enclosed buildings decreased significantly as the optimization progressed. This may be because the initial setting of this study specifies that each building type does not appear repeatedly, thereby affecting the optimization process. However, the results obtained in this study align with those of Taleghani et al., who found that enclosed buildings exhibit the lowest demand for heat energy and offer the longest period of thermal comfort during the summer [23]. Hybrid buildings (H–1) appear the least frequently. The 1518th generation solution, which is closest to the ideal point, consists of slab buildings and enclosed buildings, with no hybrid or point buildings. The trend of the solutions shifts from high BEC and high FAR in the early stage, to low BEC and low FAR in the middle stage, and then to high BEC and high FAR in the later stage. Solutions in the later stage gradually consist ofenclosed buildings and slab buildings, which can be seen to be beneficial for improving the overall performance of BEC, FAR, and UTCI [20]. Regarding point buildings, 1–3 story point buildings appear more frequently in the middle stage, while 4–6 story point buildings appear more in the later stage. This corresponds to the trend of solutions moving towards high energy consumption and high FAR in the later stage. Most enclosed buildings face south, with approximately equal numbers facing east and west. This aligns with the findings of a study conducted in the Mediterranean climate zone, which found that compact layouts, south-oriented buildings, and enclosed block configurations represent the group layout patterns with the highest energy efficiency [50]. This may be because, under the condition that the building spacing between the six plots is fixed, E–1 and E–2 amongenclosed buildings have a larger floor area compared to other types of buildings, and their efficiency in balancing the three objectives is higher than that of other types of buildings.

In addition, we statistically analyzed the occurrence frequency of various building types in the six plots within the optimal solutions (

Table 6. Frequency of occurrence of various types of buildings on the six plots of land.

Building Type	Plot 1	Plot 2	Plot 3	Plot 4	Plot 5	Plot 6
Enclosed (E–1, E–2, E–3)	0.453	0.316	0.522	0.113	0.522	0.506
Slab (S–1, S–2, S–3, S–4)	0.539	0.664	0.328	0.814	0.223	0.304
Hybrid (H–1)	0.004	0.004	0.040	0.053	0.020	0.008
Point (P–1, P–2)	0.004	0.016	0.110	0.020	0.235	0.182

). It was found that although enclosed buildings appeared 245 times, their occurrence frequency in the central plots (i.e., Plot 2 and Plot 4) was relatively low, while slab-type buildings had a higher occurrence frequency in the central plots. This may be because in the middle of the site, the shadow coverage of slab-type buildings is continuous, and the initially set number of floors of slab-type buildings is higher, which can reduce the temperature of the ground and the surrounding environment and optimize UTCI.

4.3. Analysis of the Influence Degree of Design Variables

In recent years, linear regression analysis has seen growing application in exploring the connection between block form and energy consumption. This is largely because it enables the identification of causal links within multiple data sets by means of parallel stepwise examination of various variables [51,52] and the quantification of the impact of design variables on objective variables. Since the research target is block-scale buildings, design variables do not include window–wall ratio, exterior wall heat transfer coefficients or roofs, but mainly focus on building floor count, orientation and type [19]. To explore the impact of each design variable on the objective variables, this study performed a sensitivity analysis on 15 design variables (see Table 5). Figure 11 shows the influence of design variables on the block’s BEC, UTCI and FAR in the model. It indicates that the floor counts of S–1, S–2 and S–4 (X4, X5, X7) have the most significant impacts on BEC, UTCI and FAR: their impacts on BEC are 87.5%, 60.8% and 55% respectively; on UTCI are 48.1%, 51% and 45.4% respectively; and on FAR are 85.8%, 58.5% and 51.6% respectively. All variables have certain impacts on BEC and UTCI, but building angle has a minimal impact on BEC and no impact on FAR. This is because the calculation of FAR directly relies on the building’s base area and the number of floors (FAR = total building area/plot area). Hence, changes in the number of floors (X1–X10) have an almost decisive impact on FAR. In contrast, building orientations (X11–X15) only alter the azimuth of the building facades and do not affect the base area or the number of floors, thus having no significant effect on FAR. However, other studies have found that orientation is a key morphological factor affecting building energy consumption. For example, under local climatic conditions in the UAE, orientation serves as the primary determinant of cooling loads and BEC within urban block systems [53,54]. The reason for this discrepancy may be the difference in climatic conditions between research regions, and the fact that building orientations in this study actually refer to the three directions (east, west, south) of courtyard-style buildings. Furthermore, these design variables are unable to reflect building types. Our method involves recording six building codes for each scheme combination to determine which buildings have been selected, thereby forming a unique design scheme consisting of six different buildings.

4.4. Correlative Analysis of Urban Morphological Parameters and Objective Variables

Urban morphology has direct or indirect effects on building energy consumption and thermal comfort [55,56]. Therefore, it is essential to examine the interrelationship that exists between urban morphological parameters and BEC, etc. During the multi-objective optimization process of block morphology, this study recorded the urban morphological parameters and simulation results corresponding to each scheme. To examine the associative links between urban morphological parameters and optimization objectives, scatter plots were drawn using Origin 2024, and Pearson correlation analysis was performed using SPSS Statistics 26. The findings are illustrated in Figure 10, Figure 11 and Figure 12, and all results are significantly correlated at the 0.01 level (p < 0.01).

The results are presented in Figure 12, the correlation coefficients between the BEC and the SV, OSR, SF, AF and energy use intensity (EUI) are −0.739, −0.947, −0.915, 0.971 and 0.743, respectively. Among them, EUI represents the energy consumption intensity per unit building area. This indicates that these parameters have significant correlations with BEC. As shown in Figure 12a–c, SVF, OSR and SF are obviously negatively correlated with BEC. This is because a larger SVF means greater building spacing and fewer floors, which leads to reduced building energy consumption; OSR shows the same variation trend as SVF. In addition, the results indicate that SF shows a noticeable negative correlation with BEC, which is contrary to our common perception. This may be due to the fact that an increase in SF leads to a smaller building volume, resulting in a lower proportion of fixed energy consumption for equipment and thus a reduction in BEC. As shown in Figure 12d, the correlation coefficient between building density (BD) and BEC is 0.239, which suggests a weak correlational relationship. Meanwhile, it is found that under the condition of the same BD, the corresponding BEC has an extremely wide distribution range. This is because building density cannot reflect the vertical layout of block buildings, thus corresponding to a wider range of changes in building energy consumption, which aligns with the findings reported in a study by Jianhu [21]. As shown in Figure 12e,f, AF and EUI are obviously positively correlated with building energy consumption. This is because an increase in AF means an increase in the total number of building floors, which leads to a rise in BEC. However, this is contrary to the conclusions of previous studies, which may be related to the parameter settings of the research samples, building types, etc. The correlation between the two depends on specific contexts. Moreover, EUI is the ratio of total building energy consumption to total building area; under the condition that the total building area remains unchanged, there is a positive correlation between the two.

As shown in Figure 13, the correlation coefficients between the UTCI and SVF, OSR, SF, BD and AF are 0.804, 0.881, 0.758, −0.689 and −0.661, respectively, indicating significant correlations. The correlation coefficient between EUI and UTCI is −0.316, exhibiting a weakly correlated relationship. As presented in Figure 13a–c, SVF, OSR and SF are positively correlated with UTCI. This is because the objective variable UTCI in this study refers to outdoor thermal comfort: an increase in parameters such as SVF implies a more open outdoor sky, which enhances solar radiation, raises UTCI and reduces outdoor comfort. Similar to the above reasoning, BD corresponds to a wide range of UTCI values. As shown in Figure 13e,f, AF and EUI are negatively correlated with UTCI. This may be because an increase in AF creates increased shading among buildings, thereby causing a reduction in UTCI. Additionally, as will be discussed later, EUI is significantly positively correlated with FAR; a high EUI indicates a high FAR, and the increased density of building areas enhances shading between buildings, reduces outdoor solar radiation and thus lowers UTCI.

As shown in Figure 14, SVF, OSR, SF, AF and EUI are significantly correlated with the FAR, with correlation coefficients of −0.751, −0.964, −0.934, 0.946 and 0.679, respectively. The correlation between BD and FAR is weak, with a coefficient of 0.324. As presented in Figure 14a–c, SVF, OSR and SF are negatively correlated with FAR. This is because an increase in SVF and OSR means an expansion of non-building spaces, leading to a decrease in FAR. An increase in SF may be attributed to a reduction in building floors and a decrease in building area, which also results in a lower FAR. As shown in Figure 14d, BD is positively correlated with FAR, and the reason for the wide distribution range of FAR corresponding to BD is consistent with the above explanation: building density does not directly affect the floor area ratio, resulting in a weak correlation between the two. As depicted in Figure 14e,f, AF and EUI are positively correlated with FAR. An increase in AF implies more building floors and an expansion of building area, which raises FAR. A high FAR corresponds to high-rise and compact layouts, leading to an increase in EUI. The above results indicate that urban form-related indicators are correlated with both BEC and UTCI. Moreover, as an urban form indicator itself, FAR has certain correlations with other indicators, which are broadly in line with the results reported in numerous earlier studies [40,43,57,58].

4.5. Limitations and Future Work

This study has certain limitations that need to be addressed in future research. Firstly, the current research mainly focuses on high-temperature conditions in summer, without covering block performance under different annual climatic backgrounds, such as winter heating and transition seasons. Optimization results based on a single season cannot fully reflect the comprehensive annual performance of the block. In the future, simulations should be extended to multiple annual working conditions, with studies conducted across different seasons throughout the year. Secondly, the constraints on optimization objectives are insufficient, and the impact of different weight allocations on the Pareto frontier has not been explored. Future work will incorporate multi-dimensional constraints such as light environment (sunshine duration, solar energy potential) and economic costs. Thirdly, the universality across regions and scenarios is inadequate, with limitations in climate zones and a relatively single function of the research object. In future research, a universal model of morphological parameters across climate zones should be established. Fourthly, the multi-objective optimization algorithm Wallacei–NSGA–II has low iteration efficiency and is time-consuming, making it difficult to simulate larger-scale blocks. Machine learning algorithms like artificial neural networks (ANN) will be integrated into future studies. Fifthly, in the future, the impact of users’ activity patterns on energy consumption and thermal environment will be incorporated, and microscopic characteristics such as building facade materials and window-opening methods will be refined.

5. Conclusions

A multi-objective optimization method for urban low-density block morphology is proposed in this study, which is based on the Grasshopper platform (implementing the NSGA-II algorithm through the Wallacei plugin), aiming to optimize the design of BEC, UTCI, and FAR of commercial blocks in the hot-summer and cold-winter regions during summer. The research process includes basic data extraction and variable determination, parametric modeling and performance simulation, multi-objective optimization, data analysis, and scheme generation. The study conducts an optimal design on block morphology and obtains favorable optimization results, which confirms the validity of the method put forward in this study. The main conclusions are as follows:

(1)

Multi-objective optimization model implemented via Wallacei exhibits strong performance, with Pareto-optimal solutions evenly scattered at the leading edge of feasible solutions. Compared with dominated solutions, the three objectives performance within Pareto-optimal solutions has been improved. The optimization trends are as follows: E–1 and E–2 (enclosed buildings) always appear in the optimal solutions, while H–1 (Hybrid buildings) rarely appear. The block shifts from high BEC and high FAR in the early stage, to low BEC and low FAR in the middle stage, and then to high BEC and high FAR in the later stage. In the middle stage, 1–3 story point buildings appear more frequently, while in the later stage, 4–6 story point buildings appear more frequently. Enclosed buildings are mainly south-facing, followed by east and west orientations. The optimal solution closest to the ideal solution consists of slab buildings and enclosed buildings, with no point or hybrid buildings. The occurrence frequency of enclosed buildings is relatively low in the central plots (i.e., Plot 2 and Plot 4), while that of slab-type buildings is relatively high in these plots. Therefore, priority should be given to the combination of enclosed and slab-type buildings, while excessive use of hybrid and point-type buildings should be curbed. The shading properties of enclosed buildings can be utilized to improve thermal comfort in summer.

(2)

The multi-objective optimization model is capable of effectively optimizing the three objectives related to block buildings. Compared with the extremely inferior solution, the optimal solution closest to the ideal point reduces BEC by 33.2% and UTCI by 1.3%, and increases FAR by 102.8%. This suggests that the optimization algorithm is capable of effectively accomplishing multi-objective optimization of BEC, UTCI and FAR of block buildings, thereby providing a reusable technical paradigm for the optimization of urban morphology at the meso-scale.

(3)

The findings from the sensitivity analysis suggest that design variables exert varying degrees of influence on the three optimization objectives. Among them, the number of floors of the three slab-type buildings (S–1, S–2 and S–4) has the most significant impacts on BEC, UTCI and FAR: their impacts on BEC are 87.5%, 60.8% and 55% respectively; on UTCI are 48.1%, 51% and 45.4%, respectively; and on FAR are 85.8%, 58.5% and 51.6%, respectively. In contrast, the opening angle of enclosed buildings has a certain impact on UTCI, but has minimal influence on BEC and no impact on FAR.

(4)

The results of the correlation analysis indicate that the urban morphological parameter-energy consumption-related indicators have significant correlations with BEC, UTCI and FAR. These indicators reflect the building density of the block, the degree of shading, solar radiation in the block, etc. SVF, OSR and SF are obviously negatively correlated with BEC, while BD, AF and EUI are positively correlated with BEC. In terms of outdoor thermal comfort, SVF, OSR and SF are positively correlated with UTCI: a more open outdoor sky enhances solar radiation, which increases UTCI and reduces outdoor comfort. AF and EUI are negatively correlated with UTCI; as buildings become taller, the shaded area increases, leading to a decrease in UTCI. Among these indicators, OSR and AF have the greatest impact on FAR.

This study verifies, for the first time, the feasibility of multi-objective optimization at the scale of low-density commercial blocks, thus addressing the inadequacy of existing research in paying attention to “non-high-density” commercial forms. The core algorithm and toolchain do not rely on specific historical textures, and their core logic can be transferred to newly built commercial blocks, while the methodological framework can be extended to high-density blocks. Meanwhile, this study integrates machine learning algorithms to improve optimization efficiency, providing support for urban renewal at a larger scale.