=Paper=
{{Paper
|id=Vol-3702/paper36
|storemode=property
|title=Implementation of Swarm Intelligence Methods for Preprocessing in Nuroevolution Synthesis
|pdfUrl=https://ceur-ws.org/Vol-3702/paper36.pdf
|volume=Vol-3702
|authors=Serhii Leoshchenko,Andrii Oliinyk,Sergey Subbotin,Tetiana Kolpakova
|dblpUrl=https://dblp.org/rec/conf/cmis/LeoshchenkoO0K24
}}
==Implementation of Swarm Intelligence Methods for Preprocessing in Nuroevolution Synthesis==
https://ceur-ws.org/Vol-3702/paper36.pdf
Implementation of swarm intelligence methods for
preprocessing in neuroevolution synthesis
Serhii Leoshchenkoa, Andrii Oliinyka, Sergey Subbotina and Tetiana Kolpakovaa
a
National University “Zaporizhzhia Polytechnic”, Zhukovskogo street 64, Zaporizhzhia, 69063, Ukraine
Abstract
Using swarm intelligence techniques for data preprocessing over neuroevolution synthesis of
artificial neural networks (ANNs) can provide a number of advantages. Therefore, swarm
analysis techniques such as particle swarm optimization (PSO) or ant colony optimization
(ACO) can effectively identify the most relevant traits from multidimensional data. This
reduces the dimension of the input space, mitigating the Curse of dimension and improving
the effectiveness of ANNs training. In addition, swarm analysis methods can filter out noisy
or mismatched data points and detect outliers, increasing ANNs resistance to noisy input data
and expanding generalization capabilities.
In general, the introduction of swarm intelligence techniques for data preprocessing prior to
neuroevolution ANNs synthesis results in improved model performance, reduced
computational complexity, and improved generalization capabilities, making it an appropriate
approach in machine learning tasks.
This paper is proposed to consider the implementation of swarm intelligence methods for
preprocessing to improve the performance of neuromodel synthesis.
Keywords 1
Preprocessing, swarm intelligence, neurosynthesis, neuromodels, machine learning, artificial
neural networks
1. Introduction
Today, ANN-based neuromodels are widely used to automate and solve many processes and tasks
of human activity (industry, medicine, operational processes, etc.). ANNs are computational models
based on the structure and functioning of biological neurons in the human brain. They consist of
interconnected nodes (neurons) organized in layers, with each neuron performing simple calculations
and transmitting signals to connected neurons [1].
Let's look at how ANNs are used in diagnostic tasks.
1. Medical image analysis: Anns are used to analyze medical images such as X-rays, magnetic
resonance imaging, computed tomography, and histopathological images. Convolutional neural
networks (CNNs), a special type of deep ANNs designed for processing spatial data, are
particularly effective in tasks such as tumor detection, organ segmentation, and pathology
classification. CNNs can automatically study image features, providing accurate diagnostics and
anomaly detection [1].
2. Disease diagnosis: Anns are used to diagnose various diseases by analyzing patient data such
as symptoms, medical history, laboratory tests, and genetic information. Recurrent neural networks
(RNNs) and long-term short-term memory networks (LSTMs), thanks to their structural features
(the presence of feedback and gates, which allows to store the context/history of states), are able to
The Seventh International Workshop on Computer Modeling and Intelligent Systems (CMIS-2024), May 3, 2024, Zaporizhzhia, Ukraine
EMAIL: sergleo.zntu@gmail.com (S. Leoshchenko); olejnikaa@gmail.com (A. Oliinyk); subbotin@zntu.edu.ua (S. Subbotin);
t.o.kolpakova@gmail.com (T. Kolpakova)
ORCID: 0000-0001-5099-5518 (S. Leoshchenko); 0000-0002-6740-6078 (A. Oliinyk); 0000-0001-5814-8268 (S. Subbotin); 0000-0001-
8307-8134 (T. Kolpakova)
©️ 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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process sequential data, used for tasks such as predicting disease progression, identifying risk
factors, and diagnosing conditions such as heart disease, diabetes, and cancer [1].
3. Medical signal processing: Anns are used to process physiological signals such as an
electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG). They are
used for tasks such as detecting arrhythmias, predicting seizures, classifying sleep stages, and
analyzing muscle activity. Given the nature of the input data that is the result of clinical trials,
models that best represent time series are used, in particular the RNNs and LSTM networks, which
perfectly capture time dependencies and patterns in sequential data, making them suitable for
analyzing medical signals [2].
4. Drug discovery and development: Anns are used in drug discovery and development
processes, including virtual screening, molecular modeling, and drug toxicity prediction. They are
used to analyze chemical structures, predict drug-target interactions, and develop new compounds
with desired pharmacological properties. ANN-based models help speed up the drug search
process and reduce the time and cost associated with developing new drugs [2].
5. Predictive and predictive modeling: ANNs are used to build predictive and predictive models
in healthcare that help clinicians make informed decisions about patient management and
treatment strategies. These models predict outcomes such as disease progression, treatment
response, and patient survival based on clinical data, biomarkers, and other relevant factors [3].
Overall, ANNs are versatile diagnostic tools that offer the ability to analyze different types of data,
extract meaningful patterns, and make accurate predictions, thereby improving medical decision-
making and patient care.
Preprocessing refers to a set of methods applied to raw data before it is passed to a machine
learning model, such as an ANN. These methods are aimed at converting data to a format that is more
appropriate to the chosen training method, and at improving the overall performance and effectiveness
of the model. Preprocessing includes steps such as data cleaning, normalization, object scaling,
dimensionality reduction, and processing missing values or outliers [4].
Pretreatment is crucial for Ann synthesis and training for several reasons.
1. Normalization and scaling: Anns often work better when input objects are normalized or
scaled to a similar range. Preprocessing techniques, such as minimum-maximum scaling or Z-
score normalization, ensure that features make an equal contribution to the learning process,
preventing certain features from dominating the learning process due to differences in scale [5].
2. Preprocessing methods allow you to process missing values or outliers in a data set.
Imputation methods can be used to fill in missing values, while outlier detection and removal
methods help ensure that extreme values do not have an excessive impact on model behavior [5].
3. Object design and selection: preprocessing allows for object design and selection, which
removes irrelevant or redundant objects and creates new objects to better represent basic patterns
in the data. This helps improve Model generalization and reduce overtraining [5].
4. Dimensionality reduction: large-size data can create problems for ANNs in terms of
computational complexity and retraining. Dimensionality reduction techniques, such as Principal
Component Analysis (PCA) or feature selection algorithms, help reduce the number of input
measurements while preserving as much information as possible, thereby improving the efficiency
and speed of the model [5].
5. Data balancing: classification problems with unbalanced class distributions can use
preprocessing techniques such as resampling or understated sampling to balance the data set,
ensuring that the model learns from representative samples of each class [5].
In general, preprocessing plays a crucial role in preparing data for Ann synthesis and training,
ensuring that the model assimilates data efficiently, generalizes well to invisible instances, and
provides reliable and accurate predictions.
Swarm intelligence is an area of research inspired by the collective behavior of decentralized, self-
organizing systems in nature, such as ant colonies, flocks of birds, and flocks of fish. Swarm
intelligence techniques aim to solve complex problems by coordinating the actions of multiple agents,
each following simple rules, without the need for centralized control or global knowledge.
Some key characteristics of swarm intelligence include [6].
1. Self-organization: swarm-based intelligence systems exhibit emerging behavior where global
patterns and solutions arise from local interactions between individual agents without explicit
coordination.
2. Swarm intelligence relies on distributed decision-making, where each agent makes decisions
based on local information and interaction with nearby neighbors, rather than relying on
centralized control or external leadership.
3. Adaptation and reliability: swarm intelligent systems are often adaptive and resistant to
environmental changes or disturbances. They can self-adjust environments and reorganize in
response to dynamic conditions, providing stability and flexibility.
4. Research and Operation: Swarm Intelligence techniques combine exploration of the search
space to identify new solutions and use known solutions to effectively optimize performance.
Swarm intelligence techniques can be applied to neurosynthesis, which involves computer-aided
design or synthesis of artificial neural networks (ANNs) using optimization techniques. These
methods can help you explore the wide range of possible neural network architectures, optimize
network parameters, and improve network performance. Some uses of swarm intelligence techniques
for neurosynthesis include [7]:
1. Self-organization: swarm-based intelligence systems exhibit emerging behavior where global
patterns and solutions arise from local interactions between individual agents without explicit
coordination.
2. Swarm intelligence relies on distributed decision-making, where each agent makes decisions
based on local information and interaction with nearby neighbors, rather than relying on
centralized control or external leadership.
3. Adaptation and reliability: swarm intelligent systems are often adaptive and resistant to
environmental changes or disturbances. They can self-adjust environments and reorganize in
response to dynamic conditions, providing stability and flexibility.
4. Research and Operation: Swarm Intelligence techniques combine exploration of the search
space to identify new solutions and use known solutions to effectively optimize performance.
5. Swarm intelligence techniques can be applied to neurosynthesis, which involves computer-
aided design or synthesis of artificial neural networks (ANNs) using optimization techniques.
These methods can help you explore the wide range of possible neural network architectures,
optimize network parameters, and improve network performance. Some uses of swarm intelligence
techniques for neurosynthesis include [8]:
6. Swarm intelligence techniques such as particle swarm optimization (PSO), ant colony
optimization (ACO), or genetic algorithms (GAs) can be used to find optimal neural network
architectures by exploring the space of possible configurations, including the number of layers,
neuron types, connectivity patterns, and more. hyperparameters.
7. Swarm intelligence techniques can optimize neural network hyperparameters, such as
learning speed, activation functions, regularization parameters, and network topology, to improve
performance and generalization.
8. Swarm intelligence algorithms can help optimize the learning process of neural networks by
tweaking parameters related to optimization algorithms (such as gradient descent options),
convergence criteria, and data preprocessing techniques, resulting in faster convergence and
improved performance.
9. Ensemble training: swarm intelligence can be used to optimize the creation of ensemble
models that train and combine multiple neural networks to improve prediction accuracy and
reliability.
Overall, swarm intelligence techniques offer powerful neurosynthesis tools to automate the design
and optimization of neural networks for a variety of applications, including pattern recognition,
classification, regression, and management tasks [8].
2. Related Works
Let's analyze why preprocessing of input data is necessary for neuroevolution synthesis of
analytical models based on ANN and what such processes are reduced to [9].
Normalization and scaling. Preprocessing techniques such as normalization and scaling ensure that
the input characteristics are at the same scale. This is important because neural networks can be
sensitive to the magnitude of input values. Data normalization prevents certain functions from
dominating the training process solely because of their scale, resulting in more stable and effective
learning [9].
Filling in missing values and monitoring emissions. Preprocessing allows you to process missing
values and outliers in input data. Missing values can be calculated using methods such as calculating
the Mean, median calculation, or interpolation. Outliers, if left untreated, can negatively affect the
learning process, distorting the studied model parameters. Preprocessing techniques such as outlier
detection and removal ensure that the neural network learns from clean and reliable data, resulting in
improved performance [9].
Data reduction and feature selection. Preprocessing allows you to develop and select objects, while
removing irrelevant or redundant objects and creating new informative objects. This process helps
reduce the size of the input space by focusing the neural network's attention on the most relevant
functions and improving its ability to generalize invisible data [10].
Data balancing. In classification problems with unbalanced class distribution, preprocessing
methods can balance a data set by over-sampling minority classes, under-sampling majority classes,
or using more advanced methods such as the Synthetic Minority over-sampling Technique (SMOTE).
Data set balancing ensures that the neural network is trained on a representative set of samples from
each class, preventing it from shifting to the majority class [10].
Noise reduction and improved data quality: preprocessing techniques can help filter out noise and
improve the overall quality of input data. This is especially important in real data sets that may
contain inappropriate or erroneous information. Noise removal ensures that the neural network
focuses on basic data patterns, resulting in more accurate and reliable predictions [10].
Overall, input preprocessing plays a crucial role in neuroevolution synthesis, ensuring that the
neural network learns from high-quality, well-structured data. This contributes to more efficient
learning, faster convergence, and better generalization performance, which ultimately leads to more
reliable and accurate neural network models [11].
Also, when studying the very practical implementation of input preprocessing, it should be noted
that such processes can help save time during neurosynthesis as follows.
Obviously, preprocessing techniques such as selecting or extracting objects can reduce the
dimension of input data by eliminating non-essential or redundant objects. With fewer input
parameters, the neuroevolution process requires less computational resources and time to study the
reduced feature space, which leads to faster processing [11].
Normalizing or scaling the input data ensures that all functions are on the same scale, preventing
certain functions from dominating the learning process based solely on their size. Normalized data
contributes to more efficient learning convergence by reducing the time required for the
neuroevolution synthesis process [11].
Preprocessing methods for handling missing values and outliers ensure that the input data is clean
and reliable. By removing or attributing missing values and outliers, the neuroevolution process can
focus on learning based on high-quality data, resulting in faster convergence and more efficient use of
computational resources.
In general, pretreatment of input data before neuroevolution synthesis helps optimize the learning
process, increases the efficiency of model training, and reduces the overall processing time required to
achieve the desired results [12].
Let's take a look at some of the most popular methods of swarm intelligence.
PSO method:
• PSO is inspired by the social behavior of flocks of birds or schools of fish;
• each candidate solution (particle) in the search space adjusts its position based on its own
experience and the most known position of its neighbors;
• PSO is relatively easy to implement and can efficiently search in large-dimensional spaces;
• the method tends to approach local optima quickly, but may have difficulty going beyond
local optima in multi-modal or deceptive landscapes.
ACO method [13], [14]:
• ACO is inspired by the search behavior of ants.
• artificial ants leave pheromone traces along the edges of the graph representing the problem
space, and the intensity of pheromone traces reflects the desirability of the path;
• ACO is excellent in combinatorial optimization problems and is particularly effective in
solving problems with discrete and undifferentiated objective functions;
• the method requires careful parameter adjustment and may suffer from slow convergence,
especially in large and complex problem spaces.
Firefly algorithm (FA) [13], [14]:
• FA is inspired by the flashing behavior of fireflies, when brighter fireflies attract others;
• the method optimizes the set of solutions by iteratively moving brighter solutions to brighter
ones in the search space;
• FA is easy to implement and requires no gradient information, making it suitable for
optimization tasks with complex and multi-modal landscapes.;
• however, in some cases, FA can suffer from slow convergence and premature convergence,
especially in multidimensional and nonlinear optimization problems.
Each of these swarm analysis methods has its own strengths and weaknesses, which makes them
suitable for different types of optimization problems and subject areas. The choice of method depends
on factors such as problem complexity, search space characteristics, computational resources, and
desired convergence properties [13], [14].
Let's compare the most well-known methods of swarm intelligence according to the following
criteria:
• basic idea: this criterion describes the natural phenomenon or behavior that inspired the
development of each method. Understanding a biological or natural process can provide insight
into how the method works and its suitability for various optimization tasks [15], [16];
• objective function: refers to the type of optimization problems that each method is primarily
designed to solve. Some methods are better suited for continuous optimization problems, while
others are excellent for combinatorial or discrete optimization problems [15], [16];
• aggregate initialization: this criterion describes how the initial set of candidate Solutions is
generated in each method. Random initialization is common, but some methods may have specific
initialization strategies [15], [16];
• environment vs. exploitation research: reflects the balance between research (finding new
areas of the solution space) and exploitation (refining known promising solutions). Different
methods may show different trends in research or exploitation [17];
• communication mechanism: this criterion refers to how information is exchanged between
individuals in a swarm. This may include mechanisms such as pheromone traces in ACO or
brightness-based attraction in FA [17];
• convergence properties: describes the speed and efficiency with which a method comes to a
solution. So, for example, some methods can converge quickly, but run the risk of getting stuck in
local optimums, while others can converge more slowly, but with better global optimization
properties [17];
• this criterion evaluates how sensitive the method is to the choice of metaparameters. Methods
that are less sensitive can be easier to configure and more reliable in various problem areas;
• implementation complexity: it reflects the ease of implementation and computational
complexity of each method. Methods with simpler implementations may require less computing
resources;
• areas of application: this criterion defines the types of optimization problems that each
method is usually applied to. Understanding typical application areas can help you choose the most
appropriate method for a particular task [18]-[21].
In the table. 1 shows the results of comparison by criteria.
Table 1
Results of comparing PSO, ACO and FA
Criteria PSO ACO FA
Social behavior of bird Foraging behavior of Flashing behavior of
Nature Inspiration
flocks or fish schools ants fireflies
Continuous
Continuous Combinatorial
Objective Function optimization
optimization problems optimization problems
problems
Population Initialization Random initialization Random initialization Random initialization
Exploration vs. Balanced exploration
Exploration focused Exploration focused
Exploitation and exploitation
Communication No direct Pheromone-based Attraction based on
Mechanism communication communication brightness
Convergence May converge quickly May suffer from
Slow convergence
Properties to local optima slow convergence
Robustness to
Moderately sensitive Sensitive Moderately sensitive
Parameter Settings
Implementation Relatively simple to Relatively simple to
Moderate complexity
Complexity implement implement
Continuous
Continuous Combinatorial
Application Domains optimization
optimization problems optimization problems
problems
From the results of the comparison, we can conclude that the FA – method is the most universal in
order to use it for preprocessing input data in the future.
2.1. Main features of applying the FA method
The FA method is a nature-inspired optimization method developed by Xin-she Yang in 2008. It is
inspired by the blinking behavior of fireflies, where fireflies use bioluminescence to attract partners or
prey. FA is a metaheuristic algorithm used to solve optimization problems in various fields [1], [6],
[20].
The Firefly method was proposed by Xin-she Yang in 2008.
The method was developed as a population-based optimization method inspired by the blinking
behavior of fireflies to solve optimization problems more efficiently.
Since its introduction, the FA method has gained popularity and has been applied to various
optimization problems in Engineering, Computer Science, and other fields.
The main idea of the Firefly algorithm is to simulate the blinking behavior of fireflies to find
optimal solutions to the optimization problem. The method works on the basis of the following
principles [1], [6], [20]:
attraction: fireflies are attracted to each other depending on the brightness of their flashes.
Similarly, in FA, the attractiveness of a firefly (solution) is determined by its suitability, with brighter
solutions representing better solutions;
moving towards brighter solutions: Fireflies tend to move towards brighter fireflies nearby. In FA,
each Firefly (solution) adjusts its position in the search space, moving towards brighter solutions, and
the intensity of attraction is determined by the difference in brightness and distance between the
Fireflies [1], [6], [20];
randomization and research: to help explore the search space, FA includes randomization by
adding a random component to Firefly movement. This ensures that the method does not get hung up
on local optimums and can explore different areas of the solution space.;
global optimization: FA aims to find a global optimal solution by iteratively updating Firefly
positions based on their attractiveness and distance between them. The method converges when the
Firefly positions no longer change significantly or the predefined completion criterion is met.
Overall, the FA method is a simple but effective optimization technique inspired by Firefly
behavior. It is able to effectively solve a wide range of optimization problems and is successfully used
in various fields due to its simplicity and efficiency [1], [6], [20].
Now we can dive into the intricacies of firefly optimization in more detail. The essence of the
method is clearly shown in Fig. 1.
Figure 1: Fireflies in the search space. Visibility decreases with increasing distance
3. Proposed method
Levy's flight function can be incorporated into the Firefly Algorithm (FA) to enhance its
exploration capability and improve its convergence speed. Levy's flight is a random walk process
described by Levy distribution, which exhibits heavy-tailed behavior and allows for long-distance
jumps in the search space. By incorporating Levy's flight function, the fireflies in the FA can explore
the search space more effectively, leading to better global optimization performance [1], [6], [20].
Here's how Levy's flight function can be integrated into the Firefly Algorithm [22]-[25]:
1. Step 1: Levy Flight Generation:
• at each iteration of the algorithm, Levy flights are generated for each firefly to determine their
movement direction and distance;
• Levy flights are generated using Levy's flight function, which produces step sizes according
to the Levy distribution. The step sizes are typically drawn from a Levy distribution with a
specified scale parameter.
2. Movement of Fireflies:
• the fireflies adjust their positions based on the generated Levy flights. Each firefly moves a
distance determined by Levy's flight function in a random direction;
• the movement of fireflies is guided by their attractiveness, with brighter fireflies attracting
others more strongly. Fireflies move towards brighter individuals while incorporating Levy flights
for exploration.
3. Exploration and Exploitation:
• Levy flights facilitate exploration by allowing fireflies to make long-distance jumps in the
search space, enabling the algorithm to escape local optima and explore new regions;
• at the same time, the attractiveness mechanism of the Firefly Algorithm ensures that fireflies
tend to converge towards brighter solutions, promoting exploitation of promising regions of the
search space.
4. Update Positions and Iteration:
• after adjusting their positions based on Levy flights and attractiveness, the positions of
fireflies are updated, and the algorithm proceeds to the next iteration;
• the process continues until a termination criterion is met, such as a maximum number of
iterations or convergence of solutions.
By incorporating Levy's flight function into the Firefly Algorithm, the algorithm gains enhanced
exploration capabilities, enabling it to efficiently search for global optima in complex optimization
problems. This modification can lead to improved convergence speed and better overall performance
of the algorithm across various domains [22]-[25].
4. Experimental research
For the experimental research and comparison of proposed method with another approaches was
be used the following as the training and testing data:
• RT-IoT2022 [26], [16]. The RT-IoT2022 is a data sample formed by scientists on the basis of
the Internet of things infrastructure, when a wide range of Internet of Things devices and a range
of network attacks are combined in real-time conditions. The sample covers normal and hostile
network behaviors that model a general view of real-world scenarios;
• Visegrad Group companies data (VGCD) [27], [16]. This dataset contains information about
companies from the Visegrad Group countries, which include the Czech Republic, Hungary,
Poland, and Slovakia. The Visegrad Group is a cultural and political alliance of these four Central
European countries;
• Influenza Outbreak Event Prediction via Twitter (IOEP) [28]. This dataset aims to predict
influenza outbreaks using Twitter data
The general information about datasets presented in Table 2.
Table 2
General information about datasets
Number of
Datasets Feature Type Number of Features
Instances
RT-IoT2022 Real 83 123117
VGCD Real 83 85
IOEP Real 523 75839
The meta-parameters for neuroevolution synthesis of models demonstrate at Table 4.
Table 3
The meta-parameters for neuroevolution synthesis
Metaparameter Value
Population size 100
Elite size 5%
Activation function (fitness functions) hyperbolic tangent
Mutation probability 25%
Crossover type uniform
deleting an interneuronal connection
removing a neuron
Types of mutation adding interneuronal connection
adding a neuron
changing the activation function
Clustering method k-nearest neighbors
Neighbors number 7
The results of the work present at Table 4.
Table 4
The work results of proposed method
ANN time synthesis ANN time ANN time
ANN time synthesis Error on test
Datasets (without feature synthesis synthesis
(using PSO) sample
selection) (using ACO) (using FA)
RT-IoT2022 8456 s 7225 s 6935 s 5689 s 0.91
VGCD 9555 s 8123 s 7456 s 6126 s 0.924
IOEP 9832 s 8526 s 7626 s 6751 s 0.942
5. Discussions of results
Analyzing the results obtained, it is worth noting that using any method for data preprocessing
significantly reduces the time of subsequent synthesis. However, it is the use of FA that demonstrates
the greatest optimization of synthesis time. This can be explained precisely by better selection of
informative features. After all, if features can be shortened differently during preprocessing by
different methods, the FA method helps to better track hidden relationships between data, and
therefore significantly simplify the process of forming structural relationships between really
dependent features.
6. Conclusion
At the paper examines the using swarm intelligence techniques for data preprocessing over
neuroevolution synthesis of ANN can provide a number of advantages. Therefore, swarm analysis
techniques such as particle swarm optimization or ant colony optimization can effectively identify the
most relevant traits from multidimensional data. This reduces the dimension of the input space,
mitigating the Curse of dimension and improving the effectiveness of ANNs training. In addition,
swarm analysis methods can filter out noisy or mismatched data points and detect outliers, increasing
ANNs resistance to noisy input data and expanding generalization capabilities.
The results of experiments prove that the new concept has quite acceptable (certifying) indicators
of time use during the synthesis of neural networks.
7. Acknowledgements
The work was carried out with the support of the state budget research projects of the state budget
of the National University "Zaporozhzhia Polytechnic" “Intelligent information processing methods
and tools for decision-making in the military and civilian industries” (state registration number
0124U000250).
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