Dielectric Properties of Carbon Nanotube and Their Composites

Based on Ant Colony Algorithm

Ming Xu, Fei Jia and Fan Zhang

China Building Materials Academy, Beijing 100024, China

Keywords: Simulation Biology Theory, Ant Colony Algorithm, Dielectric Property Studies, Composites, Carbon

Nanotube.

Abstract: The study of dielectric properties is critical in carbon nanotube and their composites, however it has an issue

with erroneous performance positioning. The typical Bee colony algorithm is unable to address the inaccurate

dielectric positioning issue in carbon nanotube and their composites, and the result is insufficient. With the

rapid development of modern science and technology, the exploration of new materials has become a hot

topic in the field of scientific research. In particular, carbon-based materials have attracted much attention

due to their excellent physical and chemical properties, among which carbon nanotubes have become the

focus of research due to their unique structure and superior properties. In the field of electromagnetics, the

dielectric properties of materials are an important indicator to measure their response to electromagnetic

waves, which is directly related to the application potential of materials in electronic devices, energy storage

and conversion, etc.

1 INTRODUCTION

However, there are many challenges in the design and

synthesis of carbon nanotubes and their composites,

such as the precision of structural control and the

complexity of the composite process (Qi and Li, et al.

2022). In this context, the bionic algorithm provides

a new way to solve the above problems with its

efficient and intelligent optimization strategy (Liang

and Ji, et al. 2022). In this article, we will focus on the

use of Ant Colony Optimization (ACO) to improve

the dielectric properties of carbon nanotubes and their

composites, and discuss their broad application

prospects (Wu and Shao, et al. 2022).

2 RELATED CONCEPTS

2.1 The Ant Colony Algorithm is

Described Mathematically

Since their discovery, carbon nanotubes have

attracted attention for their excellent mechanical,

electrical, and thermal properties. They are extremely

high strength and flexible, and are able to withstand

extreme stresses without breaking (Mei and Liu, et

al. 2023). At the same time, carbon nanotubes exhibit

extraordinary electrical conductivity and extremely

high thermal conductivity, which make them ideal

reinforcements for the preparation of high-

performance composites. integrated with

the function to finally judge the feasibility of the

study of dielectric properties, and the calculation is

shown in Equation (1).

)

Equation illustrates the evaluation of outliers

among them.(2).

)

Dielectric properties refer to a material's ability to

store and dissipate charge in the presence of an

electric field, and are usually measured by the

material's dielectric constant and dielectric loss

(Quan and Ji, et al. 2023). In many applications, such

as capacitors, sensors, energy storage, etc., the

(

iij

tol y t⋅ ）

()

lim( ) max( 2)

iij ij ij

yt y t

rnr

→∞

⋅= ≥ ÷

−

max( ) ( 2 ) 2( 4)

ij ij ij ij

ttt t=∂ + ⋅ +



 M

140

Xu, M., Jia, F. and Zhang, F.

Dielectric Properties of Carbon Nanotube and Their Composites Based on Ant Colony Algorithm.

DOI: 10.5220/0013536900004664

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 140-145

ISBN: 978-989-758-763-4

dielectric properties are directly related to the

performance and efficiency of the device.

Traditional methods for the preparation of carbon

nanotube composites include solution mixing

method, melt mixing method and in-situ

polymerization method (Yong, 2022). These methods

are difficult to accurately control the dispersion state

and orientation of carbon nanotubes during operation,

and are susceptible to the interference of multiple

factors, resulting in unstable performance of the final

product as shown by Equation (3).

(3)

2.2 Selection of Study of Dielectric

Properties Scheme

Hypothesis II The study of dielectric properties

function is , The weighting factor is , The

unqualified study of dielectric properties, as indicated

in Equation, is thus required by the study of dielectric

properties. (4).

(4)

The ant colony algorithm is an artificial

intelligence algorithm proposed to simulate the

behavior of ants in nature in finding food paths. Each

ant releases pheromones to leave traces on the road as

they search for food, and later ants choose their path

based on the pheromone concentration (Geng and

Zhang, et al. 2023).

(5)

After many iterations, the shortest path has the

highest concentration of pheromones, thus finding the

best path from the nest to the food source. This

algorithm shows strong optimization ability and

robustness in solving combinatorial optimization

problems (Liu and Yue, et al. 2023).

(6)

2.3 Analysis of Study of Dielectric

Properties Scheme

In the design of carbon nanotube composites, the ant

colony algorithm can be used to guide the selection of

material components, the dispersion and orientation

of carbon nanotubes, and other aspects of

optimization (Wang and Dang, et al. 2022). The

algorithm can conduct a multi-dimensional search

according to the predetermined target dielectric

performance parameters to find out the best synthesis

conditions and process parameters, effectively

improving the consistency and reliability of the final

product.

()

No t

shown in Equation(7).

)

Recent research cases have shown that carbon

nanotube composites optimized by ant colony

algorithm perform well in dielectric properties

)

The study of dielectric properties is

thoroughly examined, and the threshold and index

weight of the study of dielectric properties scheme are

established to assure the Ant colony algorithm's

correctness (Chen, 2022). The study of dielectric

properties is For example, in an experiment, a

composite formula with high dielectric constant and

low dielectric loss was successfully found by

adjusting the volatilization rate of pheromones and

the search range of ants to simulate different

preparation conditions (Xing and Hu, et al. 2022).

The development of the material not only improves

the performance of electronic devices, but also

broadens its possibilities in high-frequency

applications a

)

In summary, the design accuracy and performance

stability of carbon nanotubes and their composites

can be significantly improved with the help of the

(0)

Ft≈

() 2 7

ii i

Fd t y

=⋅→⋅



∏







()

()= ( )

ii i i

gt x z Fd w

⋅−Φ

∏



lim ( ) ( ) max( )

ii ij

gt Fd t

→∞

+≤





() ( ) 4 ( 4)

ii ij

gt Fd b ac t+↔− +





() ( )

() 4

(4)

gt Fd

No t b ac

mean t

=−



()



() lim[ () ( )]lim

iii

Zh t g t F d

→∞ →∞



()

accur t

()

unno t



min[ ( ) ( )]

( ) 100%

() ( )

gt Fd

accur t

gt Fd

=×



Dielectric Properties of Carbon Nanotube and Their Composites Based on Ant Colony Algorithm

141

bionic intelligent algorithm, ant colony algorithm (Lu

and Chen, 2022). Through this advanced algorithm,

researchers can quickly locate the optimal solution in

a huge experimental parameter space, which not only

saves a lot of time and resources, but also promotes

the application and expansion of carbon nanotubes

and their composites in electronics, energy and other

fields.

(10)

In the future, combined with other advanced

algorithms such as deep learning, it will further

promote carbon-based materials to a higher level of

performance breakthroughs, and create more amazing

scientific and technological achievements for

mankind.

3 STUDY OF DIELECTRIC

PROPERTIES OPTIMIZATION

APPROACH

In the wave of modern science and technology,

materials science is ushering in unprecedented

development opportunities. Among them, carbon

nanotubes (CNTs) have attracted much attention due

to their excellent mechanical properties, electrical

conductivity and thermal conductivity. As a new type

of high-performance material, carbon nanotube

composites have a wide range of applications,

including aerospace, automotive industry,

biomedicine and other fields. However, how to

effectively disperse carbon nanotubes evenly in the

matrix material and how to optimize their interfacial

properties have always been a difficult problem for

researchers to solve.

4 PRACTICAL EXAMPLES OF

STUDY OF DIELECTRIC

PROPERTIES

4.1 Introduction to the Study of

Dielectric Properties

In this paper, we will introduce a novel optimization

method that uses ant colony algorithm to significantly

improve the performance of carbon nanotube

composites.

Table 1: Study of dielectric properties study of dielectric

properties requirements

Scope of

application

Grade Accuracy study of

dielectric

erties

Electronics I 89.35 85.37

II 87.47 87.58

Energy

storage

I 88.19 87.58

II 88.67 85.94

Biomedical

field

I 88.18 92.13

II 93.15 87.74

The study of dielectric properties process in Table

1 is shown in Figure 1.

Figure 1: Analysis process of study of dielectric properties

Ant colony algorithm is a heuristic algorithm that

simulates the path selection mechanism of ants in the

process of foraging in nature. By mimicking the way

ants release pheromones to mark paths and gradually

volatilize over time, the algorithm can efficiently

search for the optimal solution or approximate

optimal solution. The introduction of this algorithm

into the design of carbon nanotube composites means

that the optimal distribution mode of carbon

nanotubes in the matrix material can be predicted and

guided by intelligent calculations.

4.2 Study of Dielectric Properties

First, researchers need to build a mathematical model

of the carbon nanotube distribution, which should

take into account the interaction of the carbon

nanotubes with the matrix material, the mutual

repulsion forces between the carbon nanotubes, and

the expected macroscopic performance goals.

Subsequently, the ant colony algorithm was used to

conduct multiple rounds of iterative search, and a

carbon nanotube distribution scheme was generated

()

randon t



min[ ( ) ( )]

() (

)

() ( )

gt Fd

accur t randon t

gt Fd



Simulation

biology

Analyse

Nano-tube

Composite

material

Carbon

Dielectrical

property

Ant group

algorithm

INCOFT 2025 - International Conference on Futuristic Technology

142

for each iteration, and the performance of the scheme

was evaluated through an evaluation function.

Table 2: The overall situation of the study of dielectric

properties scheme

Category Random

data

Reliability Analysis

rate

Electronics 94.05 89.32 89.63

Energy

storage

91.51 90.04 93.18

Biomedical

fiel

89.22 91.46 90.99

Mean 91.43 87.23 88.59

X6 83.04 86.03 84.32

P=1.249

4.3 Study of Dielectric Properties and

Stability

By continuously iterating and adjusting the

concentration of pheromones, the final algorithm

converges to an optimal distribution mode that

maximizes the overall performance of the composite

while ensuring good dispersion of carbon nanotubes.

Experiments show that the carbon nanotube

composites optimized by ant colony algorithm have

significant improvements in tensile strength, impact

resistance and electrical properties.

Figure 2: Evaluation model of aging performance of

different algorithms

In addition, this approach offers a high degree of

flexibility. By adjusting the algorithm parameters,

such as the number of ants, pheromone volatilization

rate, etc., it can adapt to different kinds of matrix

materials and different performance requirements.

For example, for applications that require higher

thermal conductivity, the concentration of

pheromones that help to improve the distribution

pattern of the thermal conductivity path can be

increased accordingly, resulting in a more accurate

design.

Table 3: Compares the accuracy of several study of

dielectric properties.

Algorithm Survey

data

study of

dielectric

roperties

Magnitude

of change

Error

Ant

colony

orith

89.78 86.97 90.35 89.97

Bee

colony

algorith

87.33 87.67 89.71 88.32

P 88.32 90.31 88.69 88.86

It is worth mentioning that the ant colony

algorithm shows a strong advantage in dealing with

such complex optimization problems, which can not

only find the global optimal solution, but also avoid

falling into the local optimal to a certain extent, which

is crucial for multivariate and multi-constraint

material design problems.

Figure 3: Study of dielectric properties of Ant colony

algorithm

In summary, as an intelligent design tool, ant

colony algorithm provides a new perspective and

method for the research and development of carbon

nanotube composites. Through the application of this

biomimetic algorithm, we can not only control the

material structure more precisely, but also greatly

shorten the R&D cycle, reduce costs, and promote the

further development and application of carbon

nanotube composite technology. With the continuous

improvement of algorithms and the further progress

of computer technology, it is believed that more

Dielectric Properties of Carbon Nanotube and Their Composites Based on Ant Colony Algorithm

143

material design methods based on intelligent

algorithms will be developed in the future, bringing

us more powerful and efficient new materials.

4.4 Rationality of Study of Dielectric

Properties

In the broad field of materials science, carbon

nanotubes (CNTs) are attracting attention for their

unique electrical, mechanical, and chemical

properties. These tubular structures, down to the

nanometer level, are considered ideal additives for

reinforcing composites due to their superior strength

and electrical conductivity..

Figure 4: Evaluation model of aging performance of

different algorithms

However, to realize the full potential of carbon

nanotubes, the key lies in their distribution and

orientation in the matrix material. This is where Ant

Colony Optimization (ACO) plays an important role.

In this article, we will explore in detail how ant

colony algorithms can optimize the performance of

carbon nanotube composites and analyze their far-

reaching implications. The ant colony algorithm is

derived from the simulation of ants' foraging behavior

in nature. This biomimetic algorithm makes use of the

concept of pheromones to find the optimal path by

simulating the pheromones released by ants.

Applying this algorithm to the preparation of carbon

nanotube composites, scientists are actually

simulating an intelligent search process to determine

the optimal dispersion or arrangement of CNTs in the

material.

4.5 Validity of Study of Dielectric

Properties

First, ant colony algorithms can be used to guide the

dispersion of CNTs in polymers or other matrix

materials. By imitating the path planning of ants

looking for food sources, the algorithm can

effectively guide the distribution of CNTs in the

matrix, avoid clumping, and ensure the uniformity

and performance stability of the materials. This

optimized dispersion is essential to increase the

mechanical strength of materials, especially in

aerospace or automotive manufacturing where

precise control is required.

Figure 5: Study of dielectric properties of different

algorithms

Further, when it comes to the orientation of

carbon nanotubes, the ant colony algorithm shows its

ability to adjust more finely. Orientation consistency

is a key factor in improving the electrical and thermal

conductivity of materials. By adjusting the algorithm

parameters, the researchers can simulate a strategy

similar to ants searching for the shortest path, so as to

guide the CNTs to align in a specific direction, which

is of great significance for the development of high-

tech products such as e-skin and sensors.

Table 4: Compares the efficacy of several study of dielectric

properties.

Algorithm Survey

data

study of

dielectric

erties

Magnitude

of change

Error

Ant

colony

orith

88.37 88.50 88.73 90.11

Bee

colony

algorith

89.68 91.63 90.87 87.55

P 88.28 88.44 88.97 87.48

In addition to improving macrophysical

performance, the ant colony algorithm also helps to

reduce production costs. Because the algorithm can

INCOFT 2025 - International Conference on Futuristic Technology

144

efficiently guide the distribution and orientation of

CNTs, it reduces the number of trial and error and raw

material waste in the material preparation process,

which is especially critical for large-scale production.

In addition, the implementation of the algorithm does

not require complex hardware support, which

provides a feasible technical solution for

manufacturers of all sizes.

Figure 6: Ant colony algorithm study of dielectric

properties

Of course, no technology can be perfect. Although

ant colony algorithms have shown great potential in

optimizing carbon nanotube composites, the

complexity of the algorithm itself and the limitations

of its applicability to different types of materials

remain challenges to overcome. Researchers must

constantly tune and refine algorithms to adapt to the

properties and compounding requirements of

different materials.

5 CONCLUSIONS

In summary, the ant colony algorithm, as an

intelligent optimization tool, has shown its power in

improving the performance of carbon nanotube

composites. From precise control of the

microstructure of materials to cost-effective

production, this algorithm not only broadens the

boundaries of materials science, but also opens new

doors for practical applications of high-performance

materials. With the advancement of science and

technology and the continuous improvement of

algorithms, we have reason to believe that the

application of ant colony algorithms in the field of

materials science will continue to show its far-

reaching impact.

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