Space Optimization Design of Building Environment Based on
Particle Swarm Optimization Neural Network
Ling Xia
Hunan University of Arts and Science, Changde, 415000 Hunan, China
Keywords: Particle Swarm Optimization Theory, Particle Swarm Optimization Neural Network, Space, Optimize Design,
Built Environment.
Abstract: Space optimization design plays an important role in the space of intelligent building environment, but there
is a problem of inaccurate optimization positioning. Traditional deep learning cannot solve the spatial
optimization problem in the space of intelligent building environment, and the effect is not satisfactory. With
the continuous advancement of artificial intelligence technology, its application in architectural design and
management is becoming more and more extensive. Especially in the field of spatial optimization of the built
environment, the combination of particle swarm optimization (PSO) algorithms and neural network
technology is gradually changing the way we design and use built spaces. This intelligent approach not only
improves energy efficiency and functionality, but also leads to a more comfortable and healthy environment
for occupants and occupants.
1 INTRODUCTION
Firstly, the particle swarm optimization algorithm
imitates the group behavior of bird hunting and fish
predation in nature, and finds the optimal solution to
the problem through information sharing between
individuals (Zhang and He, et al.2023). When this
algorithm is applied to spatial optimization in the
built environment, each "particle" represents a
possible solution (Wang and Liu, et al.2023). These
solutions include the layout of the building, lighting,
ventilation, and many other factors that affect comfort
and efficiency (Guo and Dong, et al.2023). By
simulating the flight patterns of individuals in a flock
of birds, the particle swarm moves in the solution
space, explores and eventually converges to the
optimal or near-optimal architectural design scheme
(Liu and Sun, et al.2023).
2 RELATED CONCEPTS
2.1 Mathematical Description of a
Particle Swarm Optimization
Neural Network
Neural networks, on the other hand, provide a
powerful machine learning tool that can analyze and
learn from large amounts of data to predict the impact
of different design decisions on building performance
(Shi and Fu, et al.2023). In the process of spatial
optimization of the built environment, it can be used
to simulate and evaluate the design scheme
represented by various particles, so as to guide the
particle swarm optimization algorithm to search for
the best solution more efficiently (Wang and Zhang,
et al.2023).
lim( ) lim max( 2)
iij ij ij
xx
yt y t
→∞ →∞
⋅= ≥ ÷
(1
)
Among them, the judgment of outliers is shown in
Equation (2).
194
Xia, L.
Space Optimization Design of Building Environment Based on Particle Swarm Optimization Neural Network.
DOI: 10.5220/0013538200004664
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 1, pages 194-199
ISBN: 978-989-758-763-4
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

2
max( ) ( 2 ) ( 4)
ij ij ij ij
tttmeant=∂ + ⋅ +


M
(2
)
Combining the technology of particle swarm
optimization algorithms and neural networks, it can
handle extremely complex optimization problems.
To sum up, the combination of particle swarm
optimization algorithm and neural network is not only
a technical innovation, but also represents the trend of
spatial optimization of the built environment to be
more intelligent and automated (Guo and Zhang, et
al.2023). As technology matures and becomes more
popular, the buildings of the future will become
smarter and more able to meet human needs for
comfort, health and efficient energy use (Zhang,
2023). This is not only an innovation in the
construction industry, but also a profound
improvement and promotion of the human living
environment and adjusting building orientation to
reduce heat loss (Hu, Li, et al.2023). Traditional
solutions to these complex problems often rely on the
designer's empirical judgment and trial-and-error
process, but now, intelligent algorithms can provide
multiple efficient solutions for designers to choose
from in a short period of time..
2
() 4 2 7
iii
F
dbact y ξ =− ⋅→⋅





(3)
2.2 Selection of Space Optimization
Design Scheme
In addition, by continuously collecting real-world
operating data from the building and feeding it back
to the neural network for learning and model iteration,
this approach is able to continuously improve and
adapt to new design challenges (Yang and Li, 2023).
()= ( )
ii i i
dy
g
txz Fd w
dx
⋅−
∏

(4)
This adaptive nature allows the building to remain
in optimal condition throughout its lifecycle, which is
critical to improving the overall efficiency of the
building and reducing maintenance costs.
1
lim ( ) lim ( ) max( )
2
ii ij
xx
gt Fd t
→∞ →∞
+≤

(5)
To improve the effectiveness of the space
optimization design reliability, all data needs to be
standardized and the result is shown in Equation (6).

() ( ) ( 4)
ii ij
gt Fd mean t+↔ +

(6
)
2.3 Analysis of Space Optimization
Design Scheme
Before the particle swarm optimization neural
network, the spatial optimization design scheme
should be analyzed in all aspects, and the spatial
optimization design requirements should be mapped
to the spatial optimization design library, and the
unqualified spatial optimization design scheme
should be
()
i
No t
eliminated. According to Equation
(6), the anomaly evaluation scheme can be proposed,
and the results is shown in Equation (7).

()
() ( )
!
()
(4)!!
ii
i
ij
gt Fd
n
No t
mean t r n r
+
=
+−

(7
)
Among them, it is

() ( )
1
(4)
ii
ij
gt Fd
mean t
+
≤
+

stated
that the scheme needs to be proposed, otherwise the
scheme integration is
()
i
Z
ht
required, and the result
is shown in Equation (8).

() [ () ( )]
iii
Z
ht gt F d π =Φ +

(8
)
For example, in a real building environment space
optimization project, the combination of particle
swarm optimization algorithm and neural network
can output the most energy-efficient and comfortable
indoor layout after considering multiple factors of the
internal and external environment of the building (Wu
Jigang and Wen Gang, 2023). At the same time, the
technology can also adjust the operating parameters
of the building in real time and respond to changes in
the external environment, such as weather changes or
changes in usage patterns, so as to achieve the
purpose of dynamic optimization.
Space Optimization Design of Building Environment Based on Particle Swarm Optimization Neural Network
195



min[ ( ) ( )]
( ) 100%
() ( )
ii
i
ii
gt Fd
accur t
gt Fd
+
=×
+


(9)
In the vast arena of modern architectural design
and planning, an algorithm called "Particle Swarm
Optimization" (PSO) has quietly emerged. This
algorithm originated from the study of bird predatory
behavior, and now it has become an indispensable
intelligent tool in the design of built environment
spaces. With its unique advantages, it plays an
important role in optimizing energy efficiency,
rationalizing space layout, and improving
environmental comfort.


min[ ( ) ( )]
() 2 ()
lim ( ) ( )
ii
ii
ii
x
gt Fd
accur t randon t
gt Fd
→∞
+
=+
+


(10)
Spatial design in the built environment is a
complex decision-making process with multiple
factors and objectives. Traditional methods rely on
the experience and intuition of designers, and it is
often difficult to reach an optimal solution. However,
the particle swarm optimization algorithm provides a
completely new solution.
3 OPTIMIZATION STRATEGY
FOR SPACE OPTIMIZATION
DESIGN
Specifically, when applying PSO to optimize the
space of the built environment, it is first necessary to
clarify the optimization goals, such as reducing
energy consumption, improving the efficiency of
space use, or enhancing the comfort and aesthetics of
indoor and outdoor environments. This is followed by
a series of constraints, including building codes, cost
control, material properties, etc. The particle swarm
algorithm will find a balance between these goals and
constraints to find the best design solution.
3.1 Introduction to Space Optimization
Design
By simulating the foraging behavior of a flock of
birds, the algorithm uses a swarm of "particles" to
explore in the design space, each particle represents a
potential solution, and the interaction and learning
mechanisms between the particles continuously push
the whole flock to evolve in the direction of a better
solution.
Table 1: Space-optimized design requirements
Scope of
application
Grade Accuracy Space-
optimized
design
Revidential
construction
I 85.00 78.86
II 81.97 78.45
Commercial
b
uildin
g
I 83.81 81.31
II 83.34 78.19
Educational
building
I 79.56 81.99
II 79.10 80.11
The space-optimized design process in Table 1 is
shown in Figure 1.
Nerve net Analysis
Building
Optimization
Circumstance
Space Design
Figure 1: Analysis process for space-optimized design
It is worth mentioning that the PSO algorithm is
able to deal with nonlinear, multimodal complex
problems. This makes it extremely resilient and
adaptable in the face of changing building
environments and individual design requirements.
Whether it is the spatial planning of commercial
complexes, the environmental layout of residential
areas, or even the optimization of energy
consumption systems for large public buildings, PSO
can provide strong decision support.
3.2 Space Optimization Design
In addition, with the advancement of computer
technology and the enhancement of data analysis
capabilities, PSO algorithms can be combined with
geographic information systems (GIS), building
information modeling (BIM) and other intelligent
algorithms to form a more powerful decision-making
framework. This integrated application not only
improves design accuracy, but also significantly
shortens the design cycle, enabling construction
projects to be completed in less time and with higher
quality.
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196

Table 2: The overall picture of the space optimization
design scheme
Category Random
data
Reliability Analysis
rate
Revidential
construction
85.32 85.90 83.95
Commercial
b
uilding
86.36 82.51 84.29
Educational
b
uilding
84.16 84.92 83.68
Mean 86.84 84.85 84.40
X6 83.04 86.03 84.32
P=1.249
3.3 Space Optimization Design and
Stability
Despite the many advantages of PSO algorithms,
their application in the spatial design of the built
environment still faces some challenges. For
example, how to transform the abstract data generated
by the algorithm into practical and actionable design
solutions, and how to deal with various uncertainties
and variability in practical applications.
Figure 2: SPACE optimization design of different
algorithms
In summary, the application of particle swarm
optimization algorithm in the spatial design of the
built environment is increasingly becoming a force to
be reckoned with. It greatly improves the scientific
and practical design through intelligence and
automation, and opens up a new way to create a more
energy-saving, efficient, comfortable and livable
building environment. With the further development
and application of future technology, the particle
swarm optimization algorithm will surely bloom
more dazzling in the field of architecture.
Table 3: Comparison of spatial optimization design
accuracy of different methods
Algorithm Surve
y data
Space-
optimize
d design
Magnitud
e of
change
Error
Particle
swarm
optimizatio
n neural
networks
85.33 85.15 82.88 84.9
5
Deep
learning
85.20 83.41 86.01 85.7
5
P 87.17 87.62 84.48 86.9
7
Therefore, future research and development
efforts need to be made in the improvement of the
algorithm itself, the deep integration with other
technologies, and the flexibility in practical
applications.
Figure 3: Spatially optimized design of particle swarm
optimization neural network
We are entering a new era driven by data and
algorithms. In the field of architecture, algorithms
have become an important force for design
innovation. Not only do they reshape the way we
perceive spaces in the built environment, but they also
offer the possibility to create more efficient,
sustainable and personalized spaces. In this article,
we will explore how algorithms are revolutionizing
the built environment and how they can impact
architects' design philosophy, construction process,
and user experience.
3.4 Rationality of Space Optimization
Design
First of all, the algorithm makes architectural design
more scientific and efficient through its precise data
processing and pattern recognition capabilities.
Space Optimization Design of Building Environment Based on Particle Swarm Optimization Neural Network
197

Figure 4: Space optimization design of different algorithms
Second, algorithms make custom designs feasible
and economical. In the past, customization often
meant high cost and time investment, but the
application of algorithms has changed all that.
Through algorithm-assisted design, architects are
able to quickly generate personalized solutions based
on the specific needs and preferences of users.
Whether residential, commercial, or public spaces,
algorithms are able to provide unique design solutions
that meet the unique requirements of different clients.
3.5 The Effectiveness of Space-
Optimized Design
Traditionally, architectural design has been a
complex creative process involving numerous
variables and uncertainties.
Figure 5: Space optimization design of different algorithms
Furthermore, algorithms play a vital role in the
building construction process. The construction
industry is facing labor shortages and pressure to
improve efficiency, and algorithm-powered
automation and robotics solutions are changing that.
For example, algorithms can help plan construction
sequences, optimize material allocation, and even
play a central role in 3D printed buildings. This not
only improves the speed and accuracy of
construction, but also reduces the labor intensity of
workers and promotes the modernization of the entire
industry.
With the help of algorithms, architects can
simulate multiple design scenarios and predict the
effects of various factors such as lighting, ventilation,
and structural stability. For example, by using
computational design tools, the energy performance
of buildings can be optimized at an early stage,
reducing the time and cost of subsequent adjustments.
Table 4: Comparison of the effectiveness of spatial
optimization design of different methods
Algorithm Surve
y data
Space-
optimize
d desi
g
n
Magnitud
e of
chan
g
e
Error
Particle
swarm
optimizatio
n neural
networks
82.21 85.92 84.59 82.8
5
Deep
learnin
g
83.73 84.23 84.41 83.5
5
P 84.20 87.39 84.76 83.9
0
Figure 6: Particle swarm optimization neural network space
optimization design
Finally, algorithms are revolutionizing the design
of the built environment in terms of how they affect
the way we experience and interact. With the
development of virtual reality (VR) and augmented
reality (AR) technologies, algorithms can create
immersive, simulated environments that allow users
to experience spaces before buildings are built. This
provides designers with valuable user feedback and
fosters a strong connection between the design and
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the user's needs. In short, in the creation and evolution
of the built environment, algorithms are not only an
auxiliary tool, but a revolutionary force that cannot be
ignored. With its limitless potential, it is driving the
development of the construction sector in a more
efficient, sustainable and individual direction. The
architecture of the future will no longer be just static
structures, they will be dynamically evolving
ecosystems, carefully woven from data and
algorithms. As experts and enthusiasts in the field of
architecture, we should embrace this change and
jointly build the future space of human life
4 CONCLUSIONS
In addition, the maintenance and management of the
built environment has become more intelligent thanks
to algorithms. Through real-time analysis of large
amounts of data, algorithms can help managers
monitor the performance of buildings, predict
maintenance needs, and automatically adjust systems
to improve efficiency. Intelligent building
management systems learn from user behavior
patterns and make adjustments accordingly, such as
adjusting indoor temperature or lighting intensity, to
provide users with a more comfortable living and
working environment.
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