Outdoor Public Space Layout Optimization Method for Primary and
Secondary Schools Based on Environmental Simulation and Ant
Colony Algorithm
Tailin Zhou, Luxi Liu, Xu Liu, Yanyi Chen and Yean Yang
China Southwest Architectural Design and Research Institute Corp.Ltd,Chengdu,610041 Sichuan, China
Keywords: Swarm Behavior Theory, Environmental Simulation and Genetic Algorithm, Space Layout Optimization,
Primary and Secondary Schools, Outdoor; Public Space.
Abstract: The space layout optimization is critical in outdoor public spaces in primary and secondary schools, however
it has an issue with erroneous performance positioning. The typical Genetic algorithm is unable to address the
optimization and positioningl issue in outdoor public spaces in primary and secondary schools, and the result
is insufficient. As a result, a Environmental simulation and Genetic algorithms-based research on the
optimization method of outdoor public space layout in primary and secondary schools is provided, and
research on the optimization method of outdoor public space layout in primary and secondary schools is
assessed. To begin, the swarm behavior theory is used to discover the influencing elements, and the indicators
are split based on the space layout optimization's needs to decrease interference factors in the space layout
optimization. The swarm behavior theory is then used to create a Environmental simulation and Genetic
algorithms space layout optimization scheme, and the outcomes of the space layout optimization are
thoroughly examined. The MATLAB simulation results reveal that, under particular evaluation conditions,
the Environmental simulation and Genetic algorithms outperforms the standard Genetic algorithm in terms of
space layout optimization accuracy and time of influencing variables.
1 INTRODUCTION
The space layout optimization is a very important part
of the outdoor public spaces in primary and secondary
schools (Wang, Liu, et al. 2022), which can make the
precise control of the aging performance (Zhang, and
Chen 2023) optimization and position faster and
faster. However, in the process (Ling, Wu et al. 2023)
of space layout optimization (Lu, Dong et al. 2022),
The space layout optimization scheme (Zhao, Xing,
et al. 2022) suffers from a lack of precision, which
has a detrimental impact (Jayanetti, Halgamuge, et al.
2021) on the space layout optimization. According to
certain researchers (Lu, 2021), the space layout
optimization scheme can be successfully analyzed
(Sun, Huang, et al. 2021) and the space layout
optimization may be supported by using (Gu, Chen,
et al. 2021) Environmental simulation and Genetic
algorithms to the study of the aging performance
assessment mode. In order to maximize the space
layout optimization scheme and confirm the model's
efficacy, a Environmental simulation and Genetic
algorithms is suggested based on this information
(Feng, and Chen 2021).
2 RELATED CONCEPTS
2.1 The Environmental Simulation and
Genetic Algorithms is Described
Mathematically
The Environmental simulation and Genetic
algorithms will improve the space layout
optimization scheme using computer technology and
the index parameters in the space layout optimization,
it is found that the unqualified value parameters in
the space layout optimization is , and the space
layout optimization scheme is integrated
with the function to finally judge the feasibility of the
i
y
i
z
(
iij
tol y t⋅ ）
Zhou, T., Liu, L., Liu, X., Chen, Y. and Yang, Y.
Outdoor Public Space Layout Optimization Method for Primary and Secondary Schools Based on Environmental Simulation and Ant Colony Algorithm.
DOI: 10.5220/0013536200004664
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 103-108
ISBN: 978-989-758-763-4
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.
103

space layout optimization, and the calculation is
shown in Equation (1).
()
0
!
lim( ) lim max( 2)
!!
iij ij ij
xx
n
yt y t
rnr
δ →∞ →
⋅= Χ≥ ÷
−
(1
)
Equation illustrates the evaluation of outliers
among them.(2).
(2)
The Environmental simulation and Genetic
algorithms combines the benefits of computer
technology and quantifies the space layout
optimization, which may increase the space layout
optimization's accuracy.
Suppose I The requirements of the space layout
optimization is that the space layout optimization
scheme is , the technique for satisfying the space
layout optimization is , and the judgment function
of the space layout optimization the scheme is
as shown by Equation (3).
() 2 7
ii i
Fd t y ξ =⋅→⋅Φ

∏



(3)
2.2 Selection of Space Layout
Optimization Scheme
Hypothesis II The space layout optimization function
is , The weighting factor is , The unqualified
space layout optimization, as indicated in Equation, is
thus required by the space layout optimization. (4).
(4
)
The full function of the space layout optimization,
according to assumptions I and II of the space layout
optimization can be obtained, and the results is shown
in Equation (5).
(5
)
To increase the efficacy of the space layout
optimization, all data must be standardized, and the
results are presented in Equation (6).

() ( ) 2( 4)
ii ij
gt Fd t+↔ +

(6
)
2.3 Analysis of Space Layout
Optimization Scheme
Before carrying out the Environmental simulation
and Genetic algorithms, the space layout optimization
scheme should be analyzed in all aspects, and the
space layout optimization requirements should be
mapped to the space layout optimization library, and
the unqualified space layout optimization scheme
should be eliminated. The anomaly assessment
system may be given using Equation (6), and the
outcomes is
()
i
No t
shown in Equation(7).

22
() ( )
()
(4)
ii
i
ij
gt Fd
No t a b
mean t
+
=+
+

(7
)
Among them, it is
specified that the scheme must be suggested;
otherwise, the scheme integration is necessary; the
outcome is illustrated in Equation (8).
(8
)
The space layout optimization is
thoroughly examined, and the threshold and index
weight of the space layout optimization scheme are
established to assure the Environmental simulation
and Genetic algorithm's correctness. The space
layout optimization is a systematic test
space layout optimization scheme that must be
thoroughly examined. If the space layout
optimization has a non-normal distribution, the space
layout optimization scheme will be influenced,
lowering the total space layout optimization's
accuracy, as stated in Equation (9).
(9
)
2
max( ) ( 2 ) 2( 4)
ij ij ij ij
ttt t=∂ + ⋅ +

 M
i
t
i
set
i
y
(0)
i
Ft≈
()
i
gt
i
w
()= ( )
ii i i
dy
g
txz Fd w
dx
⋅−Φ
∏

lim ( ) ( ) max( )
ii ij
x
gt Fd t
→∞
+≤


() ( )
1
(4)
ii
ij
gt Fd
mean t
+
≤
+

()
i
Z
ht

() lim[ () ( )]lim
iii
xx
Zh t g t F d
→∞ →∞
=+

()
i
accur t
()
i
unno t


min[ ( ) ( )]
( ) 100%
() ( )
ii
i
ii
gt Fd
accur t
gt Fd
+
=×
+


INCOFT 2025 - International Conference on Futuristic Technology
104

The analysis of the space layout optimization
scheme reveals that the scheme displays a multi-
dimensional distribution, which is consistent with
objective facts. The space layout optimization has no
directional, suggesting that the scheme has great
unpredictability, and hence it is
considered as a high analytical research. If the space
layout optimization's stochastic function is, then the
computation of equation (9) may be represented as
equation (10).

min[ ( ) ( )]
() ()
2
ii
ii
gt Fd
accur t randon t
+
=+
Γ

(10)
Among them, the space layout optimization meets
the standard requirements, owing to computer
technology that adjusts the space layout optimization,
removes duplicate and irrelevant schemes, and
supplements the default scheme, resulting in a strong
dynamic correlation of the entire space layout
optimization scheme.
3 SPACE LAYOUT
OPTIMIZATION APPROACH
To achieve the scheme optimization of the space
layout optimization, the Environmental simulation
and Genetic algorithms uses a random optimization
method for the space layout optimization and modifies
the Internet information parameters. The evolutionary
algorithm separated the space layout optimization into
multiple stages and then randomly picked alternative
methods. The space layout optimization scheme of
various space layout optimization grades is improved
and examined throughout the iterative process.
Following the completion of the optimization study,
the space layout optimization level of various schemes
is composed, and the best space layout optimization is
recorded.
4 PRACTICAL EXAMPLES OF
SPACE LAYOUT
OPTIMIZATION
4.1 Introduction to the Space Layout
Optimization
The space layout optimization in complex cases is
used as the research object, with 12 paths and a test
time of 12 hours, and the space layout optimization
scheme of the specific space layout optimization is
shown in Table 1.
Table 1: Space layout optimization space layout
optimization requirements
Scope of
a
pp
lication
Grade Accuracy space layout
o
p
timization
Facilities and
equipment
I 93.56 89.37
II 89.92 89.23
Convenient
trans
p
ortation
I 87.72 88.52
II 89.5 86.12
Green
environment
I 87.39 92
II 90.81 88.24
The space layout optimization process in Table 1.
is shown in Figure 1.
Figure 1: Analysis process of space layout optimization
The space layout optimization scheme of the
Environmental simulation and Genetic algorithms,
which includes the Genetic algorithm, is closer to the
real space layout optimization needs. The
Environmental simulation and Genetic algorithms
outperforms the Genetic algorithm in terms of logic
and accuracy of the space layout optimization. The
accuracy and reliability of the Environmental
simulation and Genetic algorithms are improved by
changing the space layout optimization scheme in
Figure 2. As a result, the evolutionary algorithm's
space layout optimization scheme has improved in
terms of speed, accuracy, and summation stability.
4.2 Space Layout Optimization
The space layout optimization scheme of the
Environmental simulation and Genetic algorithms,
which includes the Genetic algorithm, is closer to the
real space layout optimization needs. The
Environmental simulation and Genetic algorithms
outperforms the Genetic algorithm in terms of logic
and accuracy of the space layout optimization. The
()
i
randon t
Outdoor Public Space Layout Optimization Method for Primary and Secondary Schools Based on Environmental Simulation and Ant
Colony Algorithm
105

accuracy and reliability of the Environmental
simulation and Genetic algorithms are improved by
changing the space layout optimization scheme in
Figure II. As a result, the evolutionary algorithm's
space layout optimization scheme has improved in
terms of speed, accuracy, and summation stability.
Table 2: The overall situation of the space layout
optimization scheme
Category Random
data
Reliability Analysis
rate
Facilities and
equipment
90.39 90.43 90.06
Convenient
transportation
86.96 90.02 89.39
Green
environment
90.59 85.97 89.4
Mean 87.14 90.82 89.41
X6 85.82 90.94 87.77
P=1.249
4.3 Space Layout Optimization and
Stability
In order to test the Environmental simulation and
Genetic algorithms's correctness,, the space layout
optimization scheme is comprised with the Genetic
algorithm, and the space layout optimization scheme
is shown in Figure 2.
Figure 2: Optimization and positioningl of aging
performance of different algorithms
Figure 2 shows that the space layout optimization
of the Environmental simulation and Genetic
algorithms is higher than that of the Genetic
algorithm, but the error rate is lower, indicating that
the Environmental simulation and Genetic
algorithms's space layout optimization is relatively
stable, whereas the Genetic algorithm's space layout
optimization is uneven. Table 3 depicts the average
space layout optimization scheme of the three
methods discussed previously.
Table 3: Compares the accuracy of several space layout
optimization.
Algorithm Surve
y data
space
layout
optimizatio
n
Magnitud
e of
change
Erro
r
Environment
al simulation
and Genetic
al
g
orithms
88.1 90.4 88.51
83.2
5
Genetic
algorith
m
91.34 91.63 91.6
88.0
5
P
90.23 89.46 90.02
90.8
6
Table 3 shows that the Genetic algorithm has
flaws in the accuracy of the space layout
optimization, and the space layout optimization varies
dramatically with a large error rate. The
Environmental simulation and Genetic algorithms
produced better space layout optimization than the ant
colony approach. At the same time, the
Environmental simulation and Genetic algorithms's
space layout optimization is higher than 90%, and the
accuracy has not altered much. To confirm the
supremacy of Environmental simulation and Genetic
algorithms. To further validate the efficiency of the
suggested technique, the Environmental simulation
and Genetic algorithms was generally examined
using various methodologies, as shown in Figure 3.
Figure 3: Space layout optimization of Environmental
simulation and Genetic algorithms
Figure 3 shows that the space layout optimization
of the Environmental simulation and Genetic
algorithms is significantly better than the Genetic
algorithm. This is because the Environmental
simulation and Genetic algorithms increases the
INCOFT 2025 - International Conference on Futuristic Technology
106

space layout optimization's adjustment coefficient
and sets the threshold of Internet information to
eliminate the space layout optimization scheme that
does not meet the requirements.
4.4 Rationality of Space Layout
Optimization
The space layout optimization scheme is integrated
with the Genetic algorithm to check the correctness
of the Environmental simulation and Genetic
algorithms, and the space layout optimization scheme
is depicted in Figure 4.
Figure 4: Optimization and positioning of aging
performance of different algorithms
Figure 4 shows that the rationality of the
Environmental simulation and Genetic algorithms's
space layout optimization is superior to that of the
Genetic algorithm, and that the rationality of the
space layout optimization can be increased by
improving the space layout optimization using the
Environmental simulation and Genetic algorithms.
With the inclusion of Environmental simulation and
Genetic algorithms, a decentralized data storage and
administration platform may be created, guaranteeing
that findings are safely stored and kept. A unique
identification may be generated for each using
Environmental simulation and Genetic algorithms,
and the appropriate data and scheme can be stored on
the Environmental simulation and Genetic
algorithms.
4.5 Validity of Space Layout
Optimization
In order to confirm the effectiveness of the
Environmental simulation and Genetic algorithms,
the space layout optimization scheme is comprised
with the Genetic algorithm, and the space layout
optimization scheme is shown in Figure 5 shown.
Figure 5: Space layout optimization of different algorithms
Figure 5 shows that the space layout optimization
of the Environmental simulation and Genetic
algorithms is higher than that of the Genetic
algorithm, but the error rate is lower, indicating that
the Environmental simulation and Genetic
algorithms's space layout optimization is relatively
stable, whereas the Genetic algorithm's space layout
optimization is uneven. Table 4 depicts the average
space layout optimization scheme of the three
methods discussed previously.
Table 4: Compares the efficacy of several space layout
optimization.
Algorithm Surve
y data
space
layout
optimizati
on
Magnitu
de of
change
Erro
r
Environmen
tal
simulation
and Genetic
al
g
orithms
94.04 90.97 88.2
88.9
7
Genetic
al
g
orith
m
90.51 88.07 89.48
93.1
9
P
88.3 90.69 88.84
87.9
4
Table 4 shows that the Genetic algorithm has
flaws in the accuracy of the space layout optimization
in terms of space layout optimization, and the space
layout optimization varies dramatically and has a high
error rate. The Environmental simulation and Genetic
algorithms produced better space layout optimization
than the ant colony approach. At the same time, the
Outdoor Public Space Layout Optimization Method for Primary and Secondary Schools Based on Environmental Simulation and Ant
Colony Algorithm
107

Environmental simulation and Genetic algorithms's
space layout optimization is higher than 90%, and the
accuracy has not altered much. To confirm the
supremacy of Environmental simulation and Genetic
algorithms. The Environmental simulation and
Genetic algorithms was typically examined by
numerous approaches to further validate the efficacy
of the suggested method, as illustrated in Figure 6.
Figure 6: Environmental simulation and Genetic algorithms
space layout optimization
Figure 6 shows that the space layout optimization
of the Environmental simulation and Genetic
algorithms is significantly better than the Genetic
algorithm. This is because the Environmental
simulation and Genetic algorithms increases the
space layout optimization's adjustment coefficient
and sets the threshold of Internet information to
eliminate the space layout optimization scheme that
does not meet the requirements.
5 CONCLUSIONS
To address the issue that the space layout
optimization is not optimal, this research presents a
Environmental simulation and Genetic algorithms
that uses computer technology to enhance the space
layout optimization. Simultaneously, the correctness
and reliability of the space layout optimization are
thoroughly examined, and the Internet information
collecting is built. The findings demonstrate that the
Environmental simulation and Genetic algorithms
can increase the space layout optimization's accuracy,
and the generic space layout optimization may be
used for the space layout optimization. However, too
much emphasis is placed on the examination of the
space layout optimization throughout the
Environmental simulation and Genetic algorithms
process, resulting in irrationality in the selection of
space layout optimization indicators.
REFERENCES
Wang Zhaolin, Liu Fubing, Yang Qingyuan, E
Shixuan,&Du Ting (2022) The spatiotemporal pattern
evolution characteristics and ant colony simulation
optimization of rural residential areas in mountainous
areas - Taking Chengjiang Town, Chongqing as an
example Journal of Natural Resources, 37 (8), 20
Zhang Xin,&Chen Liuliu (2023) A trajectory planning
method for unmanned aerial vehicles in space
environment based on improved ant colony algorithm
CN202211341183.4
Ling Chang, Wu Fuqiang,&Chen Xiaofeng (2023) A two-
level layout optimization method for nose structure
based on ant colony algorithm Aerospace Computing
Technology, 53 (2), 55-59
Lu Jianxia, Dong Jiawei, Zhao Guoli,&Weng Weini (2022)
A vehicle path optimization method with road and time
window based on ant colony algorithm
CN202211216756.0
Zhao Yao, Xing Junwen,&Zhang Yufei (2022) Multi
objective structural optimization of armored vehicle
radiators based on krigin model and ant colony
algorithm Journal of Armored Soldiers (4), 6
Zhao Fei, Chen Hao, Bai Jiandong,&Liu Tie (2021)
Research on load balancing task scheduling algorithm
for remote sensing information processing based on
improved ant colony algorithm Computer Measurement
and Control, 029 (011), 183-188
Lu Hongjie (2021) Research on path planning and vehicle
scheduling algorithms for mobile robots (Doctoral
dissertation, South China University of Technology)
Sun Guanyu, Huang Jie, Xia Junbao, Cai Dingyang,&Gan
Yiran (2021) Optimization of pipeline layout for waste
liquid discharge system based on ant colony algorithm
Technology Perspective (7), 3
Gu Wenbin, Chen Zeyu, Wu Yawei,&Yuan Minghai
(2021) Research on the Improved AGV Path Planning
Algorithm for Integrating Grid Map Models Computer
Technology and Development
Feng Zixiao,&Chen Zeyu (2021) An improved AGV path
planning algorithm based on grid map model
CN113093758A
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