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METAHEURISTICS BASED ON THE BEHAVIOR OF A COLONY OF WHITE MOLES
Y.V. Danilchenko, V. I. Danilchenko, V. М. Kureichik132-1402021-08-12Abstract ▼Optimization algorithms inspired by the natural world have turned into powerful tools for solv-ing complex problems. However, they still have some disadvantages that require the study of new and more advanced optimization algorithms. In this regard, when solving NP complete problems, there is a need to develop new methods for solving this class of problems. One of these methods can be metaheuristics based on the behavior of a colony of white moles. This paper proposes a new metaheuristic algorithm called the blind white moles algorithm. This algorithm was developed based on the social behavior of blind moles in search of food and protecting the colony from intruders. The proposed solution will be able to overcome many disadvantages of conventional optimization algo-rithms, including falling into the trap of local minima or a low convergence rate. The purpose of this work is to develop an algorithm for optimizing a complex objective function. The scientific novelty lies in the development of a genetic algorithm based on the behavior of a colony of white moles for solving NP complete problems. The problem statement in this paper is as follows: to optimize the search for solutions to complex functions by applying an algorithm based on the behavior of a colony of white moles. The practical value of the work lies in the creation of a new search architecture that allows using the developed algorithm for the effective solution of NP complete problems, as well as conducting a comparative analysis with existing analogues. The fundamental difference from the known approaches is in the application of a new bioinspired search structure based on the behavior of a colony of white moles, which will allow to exclude falling into a local minimum or a low conver-gence rate. The presented results of the computational experiment showed the advantages of the pro-posed multidimensional approach to solving the problems of placing VLSI elements in comparison with existing analogues. Thus, the problem of creating methods, algorithms and software for solving NP complete problems is currently of particular relevance
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NON-PARAMETRIC METHOD FOR DETECTING BREAKDOWN OF TIME SERIES USING THE RANDOM WALKS THEORY MECHANISM
G. F. Filaretov , Z. Bouchaala2020-11-22Abstract ▼The task of the on-line detection of a sudden change in the probability properties of a time series
is considered, which is usually interpreted as the detecting task of change point the characteristics
(breakdown) in the observed stochastic process. The actuality of the development of research on this
topic is noted, which is due to the emergence of ever new applied problems where methods and algorithms
for breakdown detecting can be successfully used - in particular, when creating monitoring systems
in industry, ecology, medicine, etc. Two main varieties of methods for breakdown detecting are
discussed: parametric and nonparametric. It is noted that, although nonparametric methods, ceteris
paribus, are inferior to parametric methods in terms of efficiency (the speed of breakdown detecting),
they also have a number of advantages, without requiring, in particular, for their application detailed
information about the probabilistic properties of the controlled process. This is fundamentally important
for building monitoring systems, when detailed information about these properties may either be completely
absent and then it is necessary to conduct a rather laborious preliminary study of it, or to be
unreliable. An original sequential nonparametric algorithm for detecting discord is proposed based on
the implementation of the random walk mechanism or, more specifically, using the theory of success
runs. The operating principle of the control algorithm is explained and its description is given. The results
of the study of the basic statistical characteristics of the algorithm, including the determination of
its effectiveness, and results of comparison with known parametric methods, are given. The area of possible
practical use of the proposed algorithm is highlighted, where its effectiveness remains quite high.
The prospects of using the proposed algorithm as part of the software and algorithmic support of monitoring
systems for various purposes are noted. -
MULTILEVEL APPROACH FOR HIGH DIMENSIONAL 3D PACKING PROBLEM
V. V. Kureichik, А. Е. Glushchenko2020-07-20Abstract ▼The article considers one of the important combinatorial optimization problems, the problem
of 3D packing of different elements in a fixed volume. It belongs to the class of NP-complex and difficult
optimization problems. The paper presents and describes the formulation of the 3D packing
problem, introduces a combined objective function that takes into account all the restrictions. Due to
the complexity of this task, a multilevel approach is proposed. It is consisting in dividing the 3D packing
problem into 3 subtasks and solving each subtask in a strict order. Moreover, for each of the
subtasks a unique set of objects is defined that are not repeated in the remaining subtasks. To implement
a multi-level approach, the authors developed a combined bio-inspired algorithm based onevolutionary and genetic search. This approach can significantly reduce the time to obtain the result,
partially solve the problem of preliminary convergence of the algorithms and obtain sets of quasioptimal
solutions in polynomial time. A software package was developed and computer-based algorithms
for automated 3D packaging based on a combined bio-inspired search were implemented.
A computational experiment was conducted on test examples (benchmarks). The packaging quality
obtained on the basis of the developed combined bio-inspired algorithm is on average 5 % higher
than the packaging results obtained using known algorithms, and the solution time is less than 5 % to
20 %, which indicates the effectiveness of the proposed approach. The series of tests and experiments
carried out made it possible to refine the theoretical estimates of the time complexity of the packaging
algorithms. In the best case the time complexity of the O (n2) algorithms; in the worst, O (n3). -
DEVELOPMENT OF METHODS OF OPTIMIZATION AND PARALLELIZATION OF COMPUTATIONAL PROCESSES IN QUANTUM ACCELERATORS
S. M. Gushanskiy, V. S. Potapov, V.I. Bozhich2021-08-11Abstract ▼Recently, there has been a rapid increase in interest in quantum computers. Their work is
based on the use of quantum-mechanical phenomena such as superposition and entanglement for
computing to transform input data into outputs that can actually provide effective performance
3–4 orders of magnitude higher than any modern computing devices, which will allow solving theabove and others. tasks in real- and accelerated-time scale. This article is devoted to solving the
problem of research and development of methods for optimizing quantum computing within the
framework of the application of quantum accelerators. A block diagram of a hardware accelerator
is proposed to increase the performance of simulated quantum computing. The development of the
structural diagram of the communication module of the hardware accelerator and the software
model was carried out. The relevance of these studies lies in mathematical and software modeling
and implementation of correction codes for correcting several types of quantum errors in the development
and implementation of quantum algorithms for solving classes of problems of a classical
nature. The scientific novelty of this direction is expressed in the elimination of one of the disadvantages
of the quantum computational process. The scientific novelty of this area is primarily
expressed in the constant updating and supplementation of the field of quantum research in a
number of areas, and the computer simulation of quantum physical phenomena and features is
poorly covered in the world. -
COMPARATIVE ANALYSIS OF CENTRALIZED AND DECENTRALIZED ALGORITHMS FOR THE MOVEMENT OF MULTICOPTER-TYPE UAVS
М.Y. Medvedev, V.K. Pshikhopov2022-04-21Abstract ▼The development of robotics makes their group application relevant for solving various
tasks. The effectiveness of performing the tasks of detecting and determining the coordinates of
objects by a group of robots significantly depends on the accuracy of maintaining a given formation.
In this regard, the task of determining motion planning algorithms that ensure the greatest
accuracy of maintaining a given formation is of practical interest. This article is devoted to the
study of the accuracy of maintaining the formation of a multicopter-type UAV group using a centralized
motion planning algorithm and a decentralized algorithm. The centralized algorithm uses
a master UAV, which transmits its coordinates to the slave UAVs. Based on the coordinates obtained
and the given framework of the formation, the guided UAVs plan their movement. In a decentralized
system, neighboring UAV groups transmit their coordinates to each other, on the basis
of which the movement of a separate UAV is planned. The accuracy of the control system is investigated
depending on the errors of the navigation system and the frequency of updating data on the
position of the leading or neighboring UAVs. It is assumed that the group's UAVs determine their coordinates in discrete moments of time using an external navigation system. Centralized and
decentralized algorithms are worked out by the same motion control system. The algorithms are
investigated in this article by numerical modeling methods. In the process of simulation, models of
kinematics, dynamics and actuators are taken into account, as well as models for the formation of
errors in the navigation system. It is shown that the de-centralized algorithm of group motion
planning provides higher accuracy compared to the centralized algorithm. However, the technical
implementation of a decentralized algorithm is more complicated from the point of view of organizing
a group communication system. In a centralized system, data transmission from the master
UAV to the slave should be implemented. In a decentralized system, it is required to implement
network communication. -
DEVELOPMENT OF BIOHEURISTICS FOR CREATING AN INTELLECTUAL SUBSYSTEM FOR MAKING EFFECTIVE DECISIONS OF NP-HARD AND NP-DIFFICULT COMBINATORY-LOGICAL PROBLEMS ON GRAPHS
D. V. Zaruba , E. V. Kuliev , D.Y. Zaporozhets , M. M. Semenova2021-11-14Abstract ▼The article is devoted to the solution of new topical problems that have arisen in the conditions
of the modern development of information and nanometer technologies in the field of design,
as well as the development of new innovative methods that provide effective solutions in polynomial
time. The article deals with the problem of solving NP-hard problems. The description of the
procedure for measuring the complexity of the problem is presented the features of NP-hard and
NP-difficult combinatorial logic problems are described. The main differences between the tasks
are presented, as well as the problems that one has to face when solving this type of task. The general
decision-making scheme is presented, consisting of the problem formulation; decisionmaking;
signal in automatic systems and feedback. At the second stage (formation and selection of
solutions), the solution is based on a bioinspired algorithm for finding solutions to the traveling
salesman problem. To solve this problem, a modified bioinspired algorithm based on the behaviorof an ant colony was developed. Unlike other optimization methods, metaheuristic algorithms can
find global optimal solutions for problems where there are many local solutions due to their random
nature. These reasons have led to the widespread use of such algorithms in solving various
optimization problems. Bioinspired algorithms are becoming a new revolution in the field of solving
optimization problems. The statement of the traveling salesman problem is presented, as well
as the solution of the problem on the basis of the ant algorithm. Algorithms such as genetic algorithms
and PSO can be very useful, but they still have some disadvantages in solving multimodal
optimization problems. These algorithms can find optimal solutions regardless of the physical
nature of the problem. In the framework of experimental studies, the analysis of the work of
bioinspired algorithms was carried out: the algorithm of a flock of bats, the bacterial algorithm
and the ant algorithm. -
INTELLIGENT SUBSYSTEM FOR DECISION SUPPORT BASED ON BIOLOGICALLY PLAUSIBLE ALGORITHMS FOR SELF-ORGANIZATION
E.V. Kuliev , M.P. Krivenko, М.М. Semenova, S. V. Ignatieva2021-11-14Abstract ▼The article discusses the basic concepts and definitions of decision support systems based
on self-organization. Decision Support Systems refers to a range of interactive computer systems
that help to use data, models, and knowledge to solve semi-structured, unstructured, or unstructured
problems. The diagram of the basic structure of the decision support system is shown and
described. Three main components of Decision Support Systems are considered, and a case is
described when the fourth component of a decision support system - a knowledge-based management
system - can be applied. The article offers a description of an intelligent decision support
system. Examples of specialized intelligent decision support systems include intelligent marketing
decision support systems and medical diagnostics systems, flexible manufacturing systems. The
problems associated with making optimal decisions occupy an important place in computer-aided
design and require improving methods and means of supporting optimal design processes at various
stages. Self-organization algorithms inspired by wildlife are considered. Bioinspired algorithms
are a representative class of self-organization algorithms. Bio-inspired computing mimics
nature and uses the underlying concepts and behavior of these systems to solve complex problems.
The article describes the algorithm for bats. An experimental analysis of the process of applying
the self-organization algorithm in decision-making systems is carried out. -
SOLUTIONS’ ENCODING IN EVOLUTIONARY METHODS FOR INSTRUMENTAL DESIGN PLATFORM
E.V. Kuliev, А. А. Lezhebokov, М. М. Semenova, V.A. Semenov2020-07-20Abstract ▼The article considers current issues and analyzes the problems of three-dimensional integration
and three-dimensional modeling that arise at the design stage during the solution of the
problem of optimal planning of components of large and extra-large integrated circuits and case
devices of electronic computing equipment. The main advantages of applying the principles of
three-dimensional integration are presented and described in sufficient detail, which allow efficiently
organizing the production of personalized electronics, optimally planning the configuration
of large and ultra-large integrated circuits, taking into account thermal and energy characteristics.
In the course of research, the authors developed an approach to encoding decisions based on
an intelligent mechanism, which is characterized by the presence of built-in means of control of
acceptable decisions. One of such tools that have experimentally proven their effectiveness is the
built-in mechanism of “deadly mutations”, which takes into account the status of genes and predetermined
restrictions on the final configuration of the housing of the designed device. A series of
general approaches and specific algorithms for solving the planning problem based on the results
of research by the author's team and modern approaches to solving NP-complete problems are
proposed. The most important practically significant result of the research of the indicated problem
is the developed software and instrumental design platform in the modern cross-platform Java
programming language. The selected development technology allows you to use all the main advantages
of modern multi-core and multi-processor architectures, to use software multi-threading
to implement parallel schemes for solving combinatorial problems. The software and tool platform
has a user-friendly interface, which allows you to effectively manage the process of solving the
problem of planning the components of large and ultra-large integrated circuits of threedimensional
integration by visualizing key performance indicators of algorithms on graphs and in
text statistics blocks. The developed application software made it possible to carry out a series of
computational experiments based on random data sets, as well as on open-data boron benchmarks
for such tasks. The results of experimental studies have confirmed the theoretical estimates of the
time complexity and effectiveness of the proposed approaches and algorithms, including the genetic
algorithm, which uses the new decision coding mechanism proposed in the work. -
DEVELOPMENT OF CORRECTION CODES FOR CORRECTING SEVERAL KINDS OF QUANTUM ERRORS
S.M. Gushanskiy, V. S. Potapov, V.I. Bozhich2020-10-11Abstract ▼Recently, there has been a rapid increase in interest in quantum computers. Their work is
based on the use of quantum-mechanical phenomena such as superposition and entanglement for
computing input data into output data that can actually provide effective performance 3 to 4 orders of
magnitude higher than any modern computing devices, which will solve the above and others tasks in
a natural and accelerated time scale. This article is devoted to solving the problem of research and
development of corrective codes for correcting several types of quantum errors that appear during
computational processes in quantum algorithms and models of quantum computing devices. The aim
of the work is to study existing methods for correcting various types and types of quantum errors and
to create a 3-qubit corrective code for quantum error correction. The work touches upon the tasks of
research and development of the functioning methods of quantum circuits and models of quantum
computing devices. The relevance of these studies lies in the mathematical and software modeling
and implementation of corrective codes for correcting several types of quantum errors as part of the
development and implementation of quantum algorithms for solving classes of classical problems.
The scientific novelty of this area is expressed in the exclusion of one of the shortcomings of the
quantum computing process. The scientific novelty of this area is primarily expressed in the constant
updating and addition of the field of quantum research in a number of areas, and computer simulation of quantum physical phenomena and features is poorly illuminated in the world. The aim of the work is computer simulation of a quantum computing process using the method of correcting
phase types of errors, which allows one to evaluate the own phase of a unitary gate that has
gained access to the quantum state in proportion to its own vector. -
ANALYSIS OF ENCRYPTED NETWORK TRAFFIC BASED ON ENTROPY CALCULATION AND APPLICATION OF NEURAL NETWORK CLASSIFIERS
V.A. Bukovshin, P.A. Chub, D.A. Korochentsev, L.V. Cherkesova, N.V. Boldyrikhin, O.A. Safaryan2021-02-13Abstract ▼Network traffic analysis allows you to solve many problems, such as: determining the pattern
of data transmission over the network, collecting statistics on the use of web applications,
monitoring and further researching network load, identifying potential malicious software and
network attacks, etc. 40% of Internet traffic belongs to unknown applications. This suggests that
for the area of network traffic analysis, the task of classifying applications has acquired particular
importance. Improvements in software in the field of network technologies have contributed to the
discovery of serious vulnerabilities in the implementation of some network protocols, namely TCP
and HTTP. By using network traffic analyzers, an attacker gained access to the contents of data
packets transmitted over the network. However, with the increasing qualifications of the information
community in the field of computer security, as well as with the development of network
technology standards, the analysis of network traffic has become noticeably more complicated.
The increased use of mathematical methods for protecting information, such as symmetric and
asymmetric cryptographic protocols, has led to the fact that most approaches to the analysis of
network traffic have lost their meaning and are no longer used. Therefore, the search for new
solutions to the problem of classifying network traffic, taking into account the possibility of its
encryption, is relevant. The article is devoted to the description of a new mixed approach to the
analysis of network traffic, based on the combined use of information theory and machine learning
algorithms. It also provides a comparative analysis of the proposed method with existing approaches
based on both information theory and machine learning. The aim of the research is to
develop an algorithm based on an intelligent approach to the analysis of network traffic. The proposed
algorithm is based on calculating entropy and using neural network classifiers. Research
objectives include: theoretical substantiation of the proposed approach in the field of information
theory, as well as machine learning algorithms; carrying out a structural description of the implemented
algorithms for calculating entropy and classifying applications that generate encrypted
traffic; comparative analysis of the proposed algorithm with existing approaches to the analysis of
encrypted network traffic. The result of the research is a new algorithm that allows classifying
various types of encrypted traffic with a high degree of reliability. -
ALGORITHM FOR AUTOMATIC SELECTION OF INFORMATION PROTECTION MEASURES DEPENDING ON THE RESULTS OF THE VULNERABILITY SCANNER REPORT
A.V. Anzina, A.D. Medvedeva, E.A. Emelyanov2021-02-13Abstract ▼Effective protection of information in an information system implies regular diagnostics and
monitoring of the network, computers, and applications to detect possible problems in the security
system. There are vulnerability scanners certified by the Federal Service for Technical and Export
Control for security scanning. As a result of scanning, vulnerabilities of the information system
can be identified, the elimination of which requires an immediate response, since attackers can
take advantage of the vulnerability of the information system and carry out an attack. However,
the selection of protection measures is a laborious process and requires a large amount of time,
then the problem of automating the selection of information protection measures arises. The development
of an algorithm for the automatic selection of information security measures is the main
goal in automating the work process of an information security specialist. The main tasks in the
development of the algorithm: selection of the fundamental characteristics of the vulnerability,
generation of a list of protection measures taking into account the security class of the information
system, comparison of protection measures with the selected characteristic. After analyzing the
information about vulnerabilities, the main indicator is chosen the vulnerability vector, which
includes the main metrics, the assessment of which allows the choice of protection measures. A set
of information protection measures was compared to each metric by means of expert assessment.
During the operation of the algorithm, the employee sets the vulnerability vector and the security
class of the information system as input parameters and as a result receives a list of necessary
protection measures. Thus, the automatic selection algorithm assumes a comparison of vulnerability
metrics with information protection measures, which will allow an employee to quickly select
measures based on the identified vulnerabilities. -
HYBRID METHOD OF ROUTE CONFIGURATION PLANNING ON A TERRAIN MAP UNDER CONDITIONS OF PARTIAL UNCERTAINTY
М. I. Beskhmelnov, B.К. Lebedev, О.B. Lebedev2025-04-27Abstract ▼The paper describes a hybrid algorithm for situational trajectory planning under partial uncertainty for a
two-dimensional space based on the integration of the wave and ant algorithms, which allows constructing trajectories
of minimum length in real time with simultaneous optimization of a number of other quality criteria for
the constructed path. The processes of forming a trajectory section and moving an object along it alternate at
each step. The trajectory is formed sequentially (step by step) at two levels of each step. The local visibility zone
and the region covered by it on the terrain map are formed and oriented relative to the current reference vector.
The first-level procedures sequentially form a chain of pairwise adjacent regions with localized obstacles on the
terrain map in steps. The second-level procedures form a set of trajectories for the passage of a moving object
through a region at a step. When the chain of regions merges, a terrain region is formed through which the trajectory
is laid. The entire trajectory is a set of individual trajectories for the passage of a moving object through
regions connecting its initial position with the target position. The search for a solution is carried out by a population
of agents on a solution search graph. The vertices of the set correspond to the cells of the region. Two
vertices are connected by an edge if the corresponding cells on the terrain model in the form of a discrete working
field are adjacent and the transition of the connection from one cell to another is possible. It should be noted
that the synthesis of the trajectory and the movement of a moving object under uncertainty is a complex task that
requires the integration of various sensor systems, data processing algorithms, path planning algorithms and
motion control systems. The constant development of technologies in the fields of artificial intelligence, machine
vision and robotics allows the creation of increasingly sophisticated autonomous navigation systems. However,
complete autonomy and guaranteed safety of a moving object under any conditions still remain complex tasks
for research. -
HARDWARE-ORIENTED ALGORITHM FOR FAST MULTIPLICATION OF A VECTOR BY A MATRIX KRONECKER PRODUCT
E.I. Dukhnich, A.G. Chefranov2021-02-25Abstract ▼The article discusses new algorithm to increase the efficiency of the operation of multiplying
a matrix Kronecker product (KP) by a vector. It is based on the use of the KP properties. This
operation is widely used in solving problems of processing signals, images, cryptography, etc.,
where the formation of large matrices with specified properties is performed using small size matrices.
In this case, matrices with the following properties are used: orthogonal (unitary), invertible,
involutive. Multiplying an n × n square matrix by a vector has a computational complexity of
O(n2). Therefore, with an increase in the number of elementary matrix factors, the size of the resulting
KP matrix and the complexity of multiplying it by a vector grow exponentially. This circumstance
significantly increases the time for solving applied problems. The aim of the proposed
work is to construct an algorithm that accelerates the processes of forming the KP and multiplyingthe vector by it. It is proposed to combine the process of multiplication with the process of forming
the KP. Thus, the KP matrix is not actually calculated explicitly. Instead, the KP factor matrices
are iteratively multiplied by the vector components in O(nlog2n) time with linear memory complexity.
The computational scheme with the hypercube topology for the possible hardware implementation
of the proposed algorithm is presented. It can be easily pipelined. Section 1 presents the definitions
and properties of the KP used in the synthesis of the proposed algorithm. Section 2 presents
an example with n = 8 illustrating the proposed algorithm, on the basis of which, in Section
3, a hardware-oriented structure of its implementation for arbitrary n is proposed. -
CONVERSION OF THE SEQUENTIAL INFORMATION GRAPH OF THE THOMAS ALGORITHM INTO A PARALLEL FORM
D. V. Mikhailov177-1882021-10-05Abstract ▼Many computational tasks can be represented in the form of a sequential information graph. In the general case, such an information graph cannot be reduced to a parallel form in order to speed up the execution of its operations. But if the vertices of this graph have the properties of associativity, distributivity, etc., such a graph can be transformed into a parallel-pipeline form. These transformations can be performed not only on graphs containing elementary operations - addition, multiplication, logical AND, etc. - but also over graphs containing macro operations. One example of such graphs is the information graph for solving SLAEs by the sweep method (Thomas's method). The article considers a solution for tridiagonal linear systems. The infor-mation graph of the sweep method consists of two parts: the forward move, in which the transition from the three-diagonal form to the two-diagonal form is performed, and the reverse move, in which the values of the variables are directly calculated. Despite the fact that the operations that make up the basic macro-operation of the sweep method have the property of associativity, a sim-ple transformation of the graph to a pyramidal form will not give the desired result. It is necessary to transform the basic macro operations in a special way and change what data is received on them. After that, it will be possible to bring the graph to a pyramidal form. For the reverse move, a similar transformation of the graph and its constituent base subgraphs is applied. Since in order to start computations in the reverse run, we need to complete the computations of the forward run, we should switch from two specialized types of computational blocks to one universal one, and build a universal computational structure on its basis.
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QUANTUM DEEP LEARNING OF CONVOLUTIONAL NEURAL NETWORK USING VARIATIONAL QUANTUM CIRCUIT
S.М. Gushanskiy, V. Е. Buglov167-1772021-10-05Abstract ▼Quantum computing in general and quantum deep learning represent a promising field re-lated to the research of modern methods and algorithms of quantum computing used for the pur-pose of teaching and developing new architectures of artificial neural networks. Recently, there has been a trend that research conducted in the field of quantum deep learning is becoming in-creasingly widespread among specialists. This can be explained by the fact that it has been estab-lished that quantum circuits are capable of functioning like artificial neural networks, while demonstrating the best results in solving several tasks, including, for example, the actual task of classifying objects in an image or in a video stream. Thanks to the rapid development of quantum computing in the field of deep learning, optimal solutions have been found for such urgent prob-lems as the vanishing gradient problem, finding a local minimum, improving the efficiency of large-scale parametric machine learning algorithms, eliminating decoherence and quantum er-rors, etc. Within the framework of this work, the process of functioning of a quantum variational scheme is described, its main characteristics are established, and disadvantages are identified. The key features of quantum computing, on which the process of implementing quantum deep learning with the reinforcement of a convolutional neural network is based, are also analyzed. In addition, quantum deep learning of a convolutional neural network has been carried out using a variational quantum scheme, which leads to an increase in the performance of a convolutional neural network in solving the problem of image processing, namely its classification, using a quantum computing environment. The relevance of this article consists in the implementation of a quantum deep learning algorithm with the reinforcement of a convolutional neural network for image processing, as well as the great importance of the subject of this study for the future devel-opment of quantum computing devices that can be used in artificial intelligence systems, etc., which corresponds to the priority direction of the development of domestic science
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ANALYSIS OF ADVANCED COMPUTER TECHNOLOGIES FOR CALCULATION OF EXACT APPROXIMATIONS OF STATISTICS PROBABILITY DISTRIBUTIONS
А.К. Melnikov, I.I. Levin, А.I. Dordopulo, I.V. Pisarenko6-192021-10-05Abstract ▼In the paper we consider the solution of a computationally expensive problem such as calcu-lation of statistics probability distribution with the help of modern computer technologies. To re-duce computational complexity and to provide a sufficient level of criteria efficiency not less than the specified threshold, we suggest to use Δ-exact approximations. To calculate exact approxima-tions, we use the method of second order, based on solution of a system of linear equations. Owing to this method, it is possible to calculate exact approximations for the maximum values of sample parameters for available computational resource. The most laborious part of the method of second order is the procedure of sequential detection of the vectors of possible solutions and test if the vectors belong to the set of solutions. The system solution set membership test for the vectors of possible solutions is data independent, so the algorithm can be data-parallelized. We give the al-gorithm complexity equation for calculation of exact approximations of statistics probability dis-tributions. Using this equation, we calculated the complexity of modern practical problems for the samples with the parameters (N, n) of the alphabet power and the sample size: (256,1280), (128,640), (128, 320), and (192,3200) for the accuracy of calculations =10-5. The computational complexity is 9.68·1022-1.60·1052 operations, and its average value is about 4.55·1025 operations, the number of tested vectors is 6.50·1023-1.39·1050, and the number of solutions is 4.67·1012-5.60·1025, respectively. The total solution time for clock-round duration of calculations cannot exceed 30 days or 2.592·106 sec. For the obtained complexity evaluation, we analysed abilities of modern cluster computer systems based on general-purpose processors, graphic accelerators, and FPGA-based reconfigurable computer systems. For each technology, we determined the number of computational nodes needed for calculation of exact approximations with the specified parameters during the specified time. We proved that it is impossible to obtain a solution for the required pa-rameters of exact approximations of statistics probability with the help of the reviewed modern computer technologies. In conclusion, we claim that it is necessary to analyse the abilities of ad-vanced computer technologies based of quantum and photonic computers, and also hybrid com-puter systems for calculation of exact approximations of statistics probability distributions with the specified parameters during reasonable time
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HEURISTIC GENETIC ALGORITHM FOR DIOPHANTINE EQUATIONS SOLVING
Е.Е. Polupanova, P.E. Usov115-1232022-01-31Abstract ▼The problem of diophantine equations solving is considered in this article. This problem can
be applied in cryptography and cryptanalysis. The description of the genetic algorithm solving
diophantine equations is stated briefly in the article. The rule of calculation the value of fitness
function of chromosome is determined, the coding system in the genetic algorithm is described.
The genetic operators used in the algorithm are mentioned and the conditions for their execution
are determined. The criterion for stopping the genetic algorithm is described. One of the shortcomings
of the genetic algorithm is analyzed. The shortcoming of the algorithm lies in its attempts
to solve any diophantine equation, including one that has no solutions. A method eliminating this
shortcoming in some cases is proposed. This method is based on number theory. An explanation is
given in which cases this method will be used. The definition of residue and nonresidue of fixed
power for fixed modulus is given before describing this method. After describing this method the
implementation of the algorithm for solving diophantine equations and systems of them is described
in detail. Then the results of experimental studies of the time and quality of the genetic
algorithm are presented. Then the result of the algorithm is presented for an equation that has no
solutions and for a system of equations that also has no solutions, but in which the total number of
unknowns is too large for the proposed method to work. The algorithm running time is compared
when solving an equation and when solving a system of equations. The conclusion is made about
the usefulness of the proposed method in solving diophantine equations and systems of diophantine
equations. -
ALGORITHM FOR TRAINING THE ARTIFICIAL NEURAL NETWORK OF FACTOR PREDICTING THE POWER CABLE LINES INSULATING MATERIALS LIFE
N.K. Poluyanovich, M. N. Dubyago2021-07-18Abstract ▼The article is devoted to the research of thermofluxtual processes in accordance with the
theory of thermal conductivity for solving the problems of factor prediction of the residual life of
insulating materials based on the non-destructive temperature method. The relevance of the task of
developing algoritma for predicting the temperature of SCL cores in real time based on the data of
the temperature monitoring system, taking into account the change in the current load of the line
and external heat removal conditions, is justified. The experimental method revealed the types of
artificial neural networks, their architecture and composition, which provide maximum prediction
accuracy with a minimum set of significant factors. A neural network has been developed to determine
the temperature regime of the current-carrying core of the power kawhite. The minimum
set of significant factors and the dimension of the input training vector is determined, which provides
the versatility of the neural network prediction method. A neural network for determining the
temperature mode of the current-carrying core is designed to diagnose and predict the electrical
insulation (EI) life of a power cable. The model allows assessing the current isolation state and
predicting the residual resource of the SCL. Comparative analysis of experimental and calculated
characteristics of learning algorithms of isostic neural is carried out. It has been found that the
proposed algorithm of artificial neural network can be used for prediction of current-carrying
core temperature mode, three hours in advance with accuracy up to 2.5% of actual value of core
temperature. The main field of application of the developed neural network for determining the
temperature mode of the current-carrying core is in di-agnostics and predicting the electrical
insulation (EI) life of the power cable. The development of an intelligent system for predicting the
temperature of the LCS core contributes to the planning of the operation modes of the electric
network in order to increase the reliability and energy efficiency of their interaction with the integrated
energy system. -
AN EVOLUTIONARY ALGORITHM FOR SOLVING THE DISPATCHING PROBLEM
V. V. Kureichik, A. E. Saak, Vl.Vl. Kureichik2021-07-18Abstract ▼The paper considers one of the most important optimization tasks – the dispathing task that belongs
to the class of NP-complex optimization problems. The paper presents the formulation of this
problem. In Grid systems the array of users' requests for computer services is modelled by an extended
linear polyhedral of coordinate resource rectangles. In this case, dispatching is represented
by the localization of a linear polyhedron in the envelope of the area of computational and
time resources of the system according to the multipurpose criterion of the quality of the applied
assignment. Due to the complexity of this problem, the authors propose methods of evolutionary
modelling for its effective solution and describe a modified evolutionary search architecture.
Three additional blocks are introduced as a modification. This is a block of "external environment",
a block of evolutionary adaptation and a block of "unpromising solutions." The authors
have developed a modified evolutionary algorithm that uses the Darwin’s and Lamarck’s evolution
models. This makes it possible to significantly reduce the time for obtaining the result, partially
solve the problem of premature convergence of the algorithm, and obtain sets of quasi-optimal
solutions in polynomial time. A software module has been developed in the C # language. A computational
experiment has carried out on test examples and shown that the quality of solutions
obtained on the basis of the developed evolutionary algorithm is, on average, 5 percent higher
than the results of solutions obtained using the known algorithms of sequential, initial-ring and
level at comparable time, which indicates the effectiveness of the proposed approach. -
ALGORITHM FOR OPTIMAL CONTROL THE DIGITAL TWIN OF THE ENTERPRISE
S.N. Masaev2021-08-11Abstract ▼The volume of processed information increases when analyzing and control the activities of an
enterprise as a system. The amount of processed information directly depends on the dimension of
this system. In the work, the activity of the enterprise is formalized as a digital twin of the enterprise.
The digital twin of the enterprise is analyzed as a dynamic system. The enterprise was identified as a
dynamic system. The digital twin of the enterprise is formalized as V. Leontiev's balance model. An
algorithm for optimal control of the digital twin of the enterprise has been created.
The following functions are considered as parameters of optimal control: the trajectory of the system,
the execution time of the algorithm and the indicator of the state of the system. In the algorithm for
enterprise control, the following methods were used: Bloom's taxonomy, the competence of graduates
in the SFU specialties and the National Qualifications Framework of the Russian Federation. The
identification of the enterprise processes is carried out by the method for which the patent has been
obtained. The algorithm is implemented in the author's software package for analyzing a system with
a dimension of 1.2 million values. The study showed significant changes in the values of the optimal
control functions characterizing the states of a dynamic object, depending on the selected techniques.
Calculations have shown how the choice of control method affects the optimality of decisions. The
state of the enterprise is displayed through the competencies of the personnel: psychomotor, cognitive
and affective. It was found that with low cognitive and affective abilities of the staff, psychomotor
activity begins to prevail, which leads to little result. With the growth of the cognitive abilities of thepersonnel, psychomotor activity becomes more adequate to the internal tasks and the influence of the
parameters of the external environment. An integral indicator was used to assess the implementation
of methods in enterprise control. The estimation of the optimality of the solution for control the digital
twin of the enterprise as a dynamic system is carried out. -
DEVELOPMENT OF ALGORITHMS OF INTELLIGENT SERVICE FOR INFORMATION SEARCH AND MONITORING
M. S. Anferova, A. M. Belevtsev2021-08-11Abstract ▼This paper describes the problem of strategic analysis and the choice of directions for the development
of an innovative enterprise in the conditions of transition to the 6th technological order and
industry 4.0. In these conditions, search and analytical processing of information cannot be fully performed
without the use of automated information and analytical systems, including those based on artificial
intelligence. During the analysis, the main priority functions that the developed services should
provide were identified. The main difficulties in the development of these services are identified, such as:
pre-processing of data and automated checking of the relevance of databases. To effectively solve thetasks set, the intelligent monitoring and information retrieval service should use an integrated approach,
taking into account the effectiveness of applying methods for individual subtasks, and ensure high efficiency
of implementing all stages of the intelligent monitoring procedure. In this regard, this paper describes
not only the development of a general intelligent search algorithm, but also individual block
algorithms necessary to ensure the priority functions of the service being developed. The paper presents
the following algorithms: an information search algorithm necessary to solve the problem of full-text
search of documents within the database of information resources of the information and analytical
complex; an algorithm for the procedure for entering new documents; an algorithm for pre-processing
data that includes stemming and removing punctuation marks for subsequent text analysis; an algorithm
for evaluating the ranking and relevance of information, including vectorization of documents; an algorithm
for clustering information search results based on the Kohonen neural network; the algorithm for
checking the relevance of information is to check whether the local copy of the document corresponds to
the current version on the source's web resource. The Python programming language for the implementation
of the presented algorithm is proposed and justified. The system provides automated continuous
monitoring with a high frequency of sending a request without the participation of an operator, which
will increase the quality and efficiency of information search in conditions of a large volume of unstructured
information. -
DEVELOPMENT OF MODIFIED METHODS AND MODELS OF SEARCH ADAPTATION FOR SOLVING THE PROBLEM OF PLANNING VLSI
O.B. Lebedev, А.А. Zhiglatiy, Е.О. Lebedevа2021-12-24Abstract ▼In this work, to solve the VLSI planning problem, a search algorithm has been developed
based on a modified ant colony method. The task of forming a VLSI plan is reduced to the task of
forming the corresponding Polish expression. The developed method for the synthesis of the Polish
expression includes the construction of a tree of cuts, the choice of the types of cuts (H or V), identification
and orientation of modules. The evolving population is split into pairs of agents. Each
member of the population is a pair of agents working together. In this case, the constructive algorithms
A1 and A2 used by the agents of the pair are different. The problem solved by Algorithm A1
is formulated as the problem of finding a one-to-one mapping Fk=M*→P of the set of modules M
with selected orientations, |M*|=|M| to the set P of positions of the template Sh. In fact, the solution
consists in choosing on the graph G1 a subset of edges E*1E1 included in the corresponding
mapping Fk. In Algorithm A2, the graph G2=(X, E2) is developed as a model of the search space
for solutions for choosing the type, sequence and location of cuts in the pattern Sh.
X={(x1i,x2i)|i=1,2,…,n} the set of vertices of the graph G2, corresponds to the set P of potential
positions of the template Sh for the possible placement of the names of the cut symbols in them.
Each potential position piP of the template Sh is modeled by two alternative vertices (x1i,x2i).
The choice of the vertex x1i when placing the cuts indicates that a cut of type V is placed in position
pi, the choice of vertex x2i indicates that a cut of type H is placed in position pi. Each iteration
l of the general algorithm includes an initial and three main stages. The initial stage is as follows.
Co-evolutionary memory matrices are nullified CEM*1 and CEM*2 are reset to zero. At the first
stage, each pair of agents dk=(a1k,a2k): – with constructive algorithms A1 and A2 he synthesizes
his solution Wk=(E1k
*,Sk); – the Polish expression Shk is formed, corresponding to the solution Wk;
– on the basis of Shk, a tree of sections Tk is formed; – on the basis of Tk, the plan Rk is formed and
the estimate of the solution Fk is calculated; – agents deposit (add) the pheromone to the cells of
the collective evolutionary memory (CEM) matrices CEM*1 and CEM*2 corresponding to the
solution edges Wk=(E1k
*,Sk) in the solution search graphs G1 and G2 in an amount proportional
to the solution estimate Fk. At the second stage, the pheromone accumulated in CEM*1 and
CEM*2 by agents of the population at iteration l is added to CEM 1 and CEM2. At the third stage,the pheromone is evaporated on the edges of the graphs G1 and G2. Tests have confirmed the
effectiveness of the proposed method. The time complexity of the algorithm, obtained experimentally,
coincides with theoretical studies and it is O(n2) for the considered test problems. -
IMPLEMENTATION OF A PROBABLE DEEP NEURAL NETWORK DECODER FOR STABILIZER CODES
S.M. Gushanskiy, V.N. Pukhovsky, V.S. Potapov2021-12-24Abstract ▼Recently, there has been a rapid increase in interest in quantum computers. Their work is
based on the use of quantum-mechanical phenomena such as superposition and entanglement for
computing to transform input data into outputs that can actually provide effective performance
3–4 orders of magnitude higher than any modern computing devices, which will allow solving the
above and other tasks in real and accelerated time scale. This work is a study of the influence of
the environment on a quantum system of qubits and the results of its implementation. A probabilistic
deep neural network decoder for stabilizer codes has been developed. The issues of error correction
for a three-bit code without state decoding are analyzed and considered. The relevance of
these studies lies in mathematical and software modeling and implementation of correction codes
for correcting several types of quantum errors in the development and implementation of quantum
algorithms for solving classes of problems of a classical nature. The scientific novelty of this direction
is expressed in the elimination of one of the disadvantages of the quantum computational
process. The scientific novelty of this area is primarily expressed in the constant updating and
supplementation of the field of quantum research in a number of areas. -
COMPUTATIONAL ASPECTS OF SOLVING GRID EQUATIONS ON GRAPHICS ACCELERATORS
N.N. Gracheva, V.N. Litvinov, N.B. Rudenko, A.V. Nikitina, А. Е. Chistyakov2021-12-24Abstract ▼To predict emergencies and irreversible consequences of human activities, scientists use
mathematical modeling. When an emergency occurs, it is very important to minimize the decisionmaking
time. The development of the project solution can be based on the forecast of changes in
the modeled process. In the numerical solution of problems of hydrophysics and biological kinetics,
it becomes necessary to develop effective methods for solving systemic equations of large dimension
with a non-self-adjoint operator. The large volume of processed information and the
complexity of computations necessitate the use of computational clusters, which include video
adapters to increase the performance of the computing system and the speed of information processing.
The aim of the research is to develop a solution for a module that implements the algorithm
of the system of linear algebraic equations (SLAE) by the modified alternative triangular
iterative method (MATM) (self-adjoint and non-self-adjoint case) using NVIDIA CUDA technology.
A method for decomposition of the computational domain in a three-dimensional case is described.
A graph model of a parallel pipeline computational process is proposed, focused on the
GPU (Graphics Processing Unit). To determine the two-dimensional configuration of flows in the
computational unit, when performing one step of one step, the MATM is minimal. The studies have
shown that the choice of the method of decomposition of the computational domain in the form of
parallelepipeds must be performed taking into account the architecture of the video adapter. The
developed algorithm and software module make it possible to more effectively use the computational
resources of the GPU used to solve computationally laborious problems of hydrophysics. -
DEVELOPMENT OF MICRO-COMMANDS AND BASIC UNITS OF THE HARDWARE ACCELERATOR OF QUANTUM CALCULATIONS
S.M. Gushanskiy, V.S. Potapov, Y.M. Borodyansky2021-02-13Abstract ▼At all stages of the development of information technology, much attention has been paid to
the issues of modeling functioning specialized high-performance computing systems, which make it
possible to provide the necessary performance indicators in combination with minimized costs of
software resources and energy consumption. The developed information system, focused on human-
machine interaction, allows you to clearly see the strengths and weaknesses of the developed
quantum computing device, to prove the advantages of its use. The developed modeling information
system is a visual aid for understanding the main methods of interaction between information
processes and information resources. A number of the most important problems cannot be
solved using classical computers, including classical supercomputers, in a reasonable time. Recently,
there has been a surge in interest in quantum computers. This article is devoted to solving
the problem of research and development of a circuit and a simulation technique for a hardware
accelerator of quantum computing. The work touches upon the problems of research and development of methods for the functioning of quantum circuits and models of quantum computing devices.
The relevance of these studies lies in the mathematical and software modeling and implementation
of the fundamental components of quantum computing models. The scientific novelty of
this direction is expressed in the optimization of the quantum computational process. The scientific
novelty of this area is primarily expressed in the constant updating and supplementing of the field
of quantum research in a number of areas. The aim of this work is to implement a technique for
constructing a hardware accelerator. The technical support of the information quantum system
and processes has been implemented, including new software for the transmission and presentation
of information. The use of a quantum computing information system differs from its counterparts
by a significant increase in the speed of solving computational problems and, most importantly,
by an exponential increase in the speed of solving NP-complete problems that can be
solved on classical machines in unacceptable time. Due to the fact that the class of NP problems is
wide, the applicability and significance of the developed method for constructing a modular system
of quantum computing is beyond doubt.
