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• MODERN APPROACHES TO SOLVING THE 3D BIN PACKING PROBLEM

  М.М. Sorokin , L. А. Gladkov , N. V. Gladkova

  131-150

  2026-09-10

  Abstract ▼

  The article is devoted to the consideration of current trends and approaches to solving the urgent optimization problem of three-dimensional bin packing problem. The importance of building effective methods for solving this problem is due to the rapid growth of e-commerce, where achieving even incremental improvements in container filling density can lead to significant reductions in freight transportation and storage costs. The article provides an analysis of various types of problems and suggests a classification of bin packing problems according to various criteria, including: offline and online packing, by dimension, by type and quantity of containers and cargo. The formulation of the classical optimization knapsack problem is given and various options for constraints due to the specifics of the tasks being solved are considered. A brief overview of the main approaches to solving the problem is given. The analysis and generalization of the characteristic features of the application of metaheuristic approaches based on the use of evolutionary and bioinspired algorithms and machine learning methods is carried out, their advantages and disadvantages are noted. Due to the complexity of the problem under consideration, it is proposed to actively use known and develop new modifications of metaheuristic algorithms that make it possible to find quasi-optimal solutions in polynomial time. The analysis of known machine learning methods and bioinspired algorithms is given, the principles of their operation are described, their main features, advantages and disadvantages are highlighted, and the prospects for their development and application to solve NP-complete combinatorial optimization problems are noted. A generalized principle of operation of metaheuristic algorithms is given. A comparative analysis of the application of various optimization methods has shown the effectiveness of using metaheuristic methods to solve the problem of three-dimensional packaging.

• IMPLEMENTATION OF CONVENTIONAL NEURAL NETWORKS ON EMBEDDED DEVICES WITH A LIMITED COMPUTING RESOURCE

  V.V. Kovalev, N.E. Sergeev

  2022-01-31

  Abstract ▼

  Large amounts of video data captured by sensor sensors in various spectral ranges, the significant
  size of convolutional neural network architectures create problems with the implementation of
  neural network algorithms on peripheral devices due to significant limitations of computing resources
  on embedded computing devices. The article discusses the use of algorithms for automatic search and
  pattern recognition based on machine learning methods, implemented on embedded devices with a
  computing resource Graphics Processing Unit. Detection convolutional neural networks «You Only
  Look Once V3» and «You Only Look Once V3-Tiny» are used as a search and pattern recognition algorithm,
  which are implemented on embedded computing devices of the NVIDIA Jetson line, located in
  different price ranges and with different computing resources ... Also, in the work, the estimates ofalgorithms on embedded devices are experimentally calculated for such indicators as power consumption,
  forward passage time of a convolutional neural network, and detection accuracy.
  On the basis of solutions implemented, both at the hardware level and in software, presented by
  NVIDIA, it becomes possible to use deep neural network algorithms based on the convolution
  operation in real time. Computational optimization methods offered by NVIDIA are considered.
  Experimental studies of the influence of computations with reduced accuracy on the speed and
  accuracy of object detection in images of the investigated architectures of convolutional neural
  networks, which were previously trained on a sample of images consisting of the PASCAL VOC
  2007 and PASCAL VOC 2012 datasets, have been carried out.

• THE ADJACENCY MATRIX RECONSTRUCTION ALGORITHM FOR CAUSAL GRAPH MODELS IN THE ABSENCE OF OBSERVABLE VARIABLES

  A. N. Tselykh , V.S. Vasilev, L. A. Tselykh

  2021-11-14

  Abstract ▼

  The paper deals with the problem of modeling complex systems in the absence of observable
  variables. To solve this problem, it is proposed to use causal graph models. The class of causal
  models considered here is defined as non-stochastic causal models with unobservable variables.
  These models are presented in the form of a directed graph, created on the basis of human mental
  representations. In this case, on the arcs, causality is expressed in the form of some marks with a
  sign that determines the direction of change in the state of the system. The considered causal models
  include heterogeneous, complex and qualitative types of variables that illustrate the nonnumerical
  nature of nodes and links and, as a consequence, the absence and impossibility of obtaining
  time series data. In the absence of observable variables and the impossibility of conducting
  experiments, the problem of reconstructing the adjacency matrix of the causal graph model becomes
  much more complicated. It is required to obtain a model with a certain spectral decomposition
  that implements the main function of the modeled system. Based on this concept, a new method
  for reconstructing the adjacency matrix is proposed, implemented on the basis of the corresponding
  causal propagation matrix or transmission matrix. The idea is to use combinatorial optimization
  based on spectral graph theory to generate data from a qualitative non-stochastic causal
  model and reconstruct an adjacency matrix using that data. In this case, the eigenvectors are
  identified as key objectives of the matrix reconstruction process, which postulates a fundamental
  approach based on the spectral properties of the graph. The results of computational experiments
  on solving the problem of reconstructing the adjacency matrix for causal graph models in the absence
  of observable variables using the developed algorithm have shown that the algorithm effectively
  reconstructs matrices from the given parameters with admissible similarity indices. The
  convergence of the approximation to the solution of the matrix reconstruction algorithm is proved
  no slower than with the speed of a geometric progression. From a technical point of view, the
  advantage of the algorithm is the implementation of a tool for automatic adjustment of the regularization
  parameter, suitable for users without prior mathematical knowledge.

• ALGORITHM OF EFFECTIVE CONTROLS FOR NONSTOCHASTIC CAUSAL MODELS IN THE ABSENCE OF OBSERVABLE VARIABLES FOR SYSTEMS OF DECISION MAKING CONTROL

  A.N. Tselykh, V.S. Vasilev , L.A. Tselykh

  2021-11-14

  Abstract ▼

  The paper deals with the problem of reproducing the decision-making process by a person under
  conditions of uncertainty and incompleteness of the initial data. The decision-maker relies on his
  belief system, which includes a shared vision of the system in relation to which the decision is being
  made. The system is presented in the form of a causal model created on the basis of human mental
  representations. These models are directed graphs, on the arcs of which the causal relationship is
  expressed in the form of labels with a sign that determines the direction of change in the state of the
  system. The vertices of this directed graph are high-level abstraction concepts. This graph simulates
  the functioning of a real system. Thus, we investigate the problem of predicting and controlling human
  actions based on non-stochastic causal models in the absence of observable variables for use in
  decision support systems and expert systems. Decision-making is considered from the point of view of
  the choice of objects of application of managerial influences - the factors of the model. In this study,
  we show that the application of the proposed algorithm can facilitate decision-making regarding the
  choice of control actions that support the achievement of the tactical and strategic goals of the decision
  maker. It should be noted that the algorithm implements an automatic selection of the regularization
  parameter, which makes the development and application of the proposed algorithm available
  to users who do not have sufficient mathematical training. The convergence of the sequence of Lagrange
  multipliers of an effective control algorithm is proved. The theorem on resonance in a nonstochastic
  causal mod-el, represented by a directed graph, which is determined by the range of admissible
  values of the damping coefficient in the control model, is proved. It is expected that the introduction
  of this tool into decision support systems will in-crease the reliability of decisions regarding
  the operation of the system as a whole. The choice of control actions using the proposed algorithm
  has high efficiency and productivity. Thus, the results presented in the study can be useful for
  developing applications in intelligent systems.

• METAHEURISTIC OPTIMIZATION METHOD BASED ON THE STEM CELL BEHAVIOR MODEL

  Y. V. Danilchenko , V.I. Danilchenko, V.M. Kureichik

  2022-05-26

  Abstract ▼

  The paper discusses optimization methods that are based on processes occurring in nature. Such
  methods have become increasingly used to solve complex problems. However, such methods have some
  drawbacks, which stimulates the development of new and more advanced optimization methods. Solving
  NP complete problems requires optimal methods that will meet all design requirements, so there is a
  need to develop new and more advanced methods for solving this class of problems. As such a method,
  the authors propose an optimization method based on a model of the behavior of stem cells in the natural
  environment. The conducted studies of the proposed method provide solutions that can overcome
  many of the shortcomings of standard optimization approaches, such as getting into the local optimum
  or low convergence rate of the algorithm based on the method under consideration. The purpose of this
  work is to develop an optimization method and an algorithm based on it for solving a complex objective
  function. The scientific novelty lies in the development of an optimization method based on the stem cell
  behavior model for solving NP complete problems. The aim of the work is to create conditions for theoptimal search for a solution to complex functions by applying the search method and, based on it, an
  algorithm for the behavior of stem cells. The practical value of the work lies in the development of a new
  metaheuristic optimization method for the efficient solution of NP complete problems. Also in the work,
  a comparative analysis with well-known competitors was carried out. The main difference of the proposed
  method from other known methods is the use of a new approach of bioinspired search based on
  the behavior of stem cells, which, as shown by practical comparison, has an advantage over known
  analogues. The results of a practical comparison of methods and algorithms based on them showed the
  advantages of the approach proposed in the work on known test functions. After analyzing the problem
  of creating methods, algorithms and software for solving NP complete problems, we can conclude that
  the development of such approaches is currently an urgent task.

• THE USE OF DISTRIBUTIVE SEMANTICS IN THE IDENTIFICATION OF SIGNIFICANT COMBINATIONS OF TITLES OF SEVERAL TEXT COLLECTIONS IN THE FORMALIZATION OF LINGUISTIC EXPERT INFORMATION

  V.I. Danilchenko, V.M. Kureichik

  2022-08-09

  Abstract ▼

  The paper discusses methods of forming special models for the representation of various sets
  of knowledge in various information systems. The work is devoted to the application of distributive
  semantics in the identification of significant combinations in one subject area (PRO) within the
  framework of the formalization of linguistic expert information (LEI). The paper applies an approach
  to the formalization of LEI based on a set of analytical methods, where linear algebra is used as
  models. This approach makes it possible to initialize the procedure for the automatic formation of
  hierarchical architectures of LEI or dendrograms when identifying significant combinations of titles
  of several collections of texts. The scientific novelty lies in the proposed analytical approach using
  distributive semantics in identifying significant combinations of titles of several collections of texts,
  which allows for the analysis and processing of linguistic expert information. A distinctive characteristic
  of the proposed approach is the ability to formalize the ABM "Global Optimization Methods"
  based on the synthesis of various already existing hierarchies of the ABM under consideration. The
  paper aims to create conditions for the formalization of the LEI by applying distributive semantics
  when identifying significant combinations of titles of several collections. The practical value of the
  work lies in the development of a new approach to the formalization of LEI, taking into account distributive
  semantics when identifying significant combinations of titles of several collections of texts.
  The ontology in owl format "Methods of global optimization" in the program "Protege" is also built
  in the work. The ontology is built on the basis of related data about. The ontology constructed in this
  work complements the search structure within the framework of the considered PRO and can be
  supplemented and developed in the future.

• HYBRID METHOD FOR SOLVING THE PROBLEM OF PLACEMENT OF DIGITAL COMPUTER DEVICES

  L. A. Gladkov , N. V. Gladkova , M.J. Yasir

  2021-11-14

  Abstract ▼

  The problem of placing elements of digital computing technology is considered in the article.
  The analysis of the current state of research on this topic is carried out, the relevance of the
  problem under consideration is noted. The importance of developing new effective methods for
  solving such problems are highlighted. The place of the placement problem in the general cycle ofthe design stage is shown. The importance of a high-quality solution to the placement problem
  from the point of view of the successful implementation of subsequent design stages is noted. The
  importance of minimizing connection delays in the design process of large-scale devices is noted.
  A review and analysis of various models and criteria for evaluating the solution to the placement
  problem is carried out. It was emphasized that the most important criterion is the length of the
  joints, it has a significant impact on the technologies used in the design. A complex mathematical
  formulation of the problem of placing elements of digital computing equipment has been completed.
  Perspective approaches to solving design problems are analyzed, hybrid methods and models
  for solving complex multicriteria optimization and design problems are described. The principles
  of operation and the model of a fuzzy logic controller are described. The description of the used
  fuzzy control scheme is given. The functions of various blocks of a fuzzy logic controller are determined.
  The structure of a multilayer neural network that implements the Gaussian function is
  proposed. The interaction of blocks of a fuzzy genetic algorithm is described. A model of a hybrid
  algorithm for solving the placement problem is proposed. The control parameters of the fuzzy
  logic controller are determined. The proposed hybrid algorithm is implemented as an application
  program. A series of computational experiments to determine the effectiveness of the developed
  algorithm and select the optimal values of the control parameters were carried out.

• A BIOINSPIRED APPROACH TO SOLVING THE PROBLEM OF 3D PACKAGING

  V.I. Danilchenko , V.V. Bova , М. М. Semenova , S.V. Ignateva , М. B. Shayliev

  2026-02-27

  Abstract ▼

  This article examines one of the most important combinatorial optimization problems – three-dimensional packaging. Optimizing three-dimensional packaging reduces costs and improves logistics efficiency, making it relevant for industry. This paper analyzes classical approaches such as greedy algorithms and dynamic programming, as well as widely used methods, including evolutionary algorithms and local search. An analysis of existing methods, including greedy search, dynamic programming, evolutionary algorithms, and local search, revealed their key characteristics and identified suitable areas of application. In the context of this analysis, an overview of the key methods that dominated during certain historical periods is presented. The analysis includes consideration of the application conditions of various methods, their effectiveness for specific types of problems, as well as their advantages and limitations.
  A multi-level search algorithm is presented that combines the advantages of traditional and modern optimization methods. This multi-level algorithm improves the accuracy of the packaging problem solution through dynamic parameter adjustment. A software package for solving the three-dimensional packaging optimization problem using bioinspired algorithms has been developed. A computational experiment was conducted on test examples (benchmarks). The packing quality obtained using the developed combined bioinspired algorithm is, on average, 7% higher than the packing results obtained using known algorithms, while the solution time is 7% to 25% shorter, demonstrating the effectiveness of the proposed approach. A series of tests and experiments allowed us to refine theoretical estimates of the time complexity of packing algorithms. In the best case, the time complexity of the algorithms is O(n²), and in the worst case, O(n³).

• CLASSIFICATION AND ANALYSIS OF EVOLUTIONARY METHODS OF EVA BLOCK LAYOUT

  Y.V. Danilchenko, V.I. Danilchenko, V. M. Kureichik

  2020-07-20

  Abstract ▼

  Currently, there is a large increase in the need for the design and development of radioelectronic
  devices. This is due to increasing requirements for radio-electronic systems, as well as
  the emergence of new generations of semiconductor devices. In this regard, there is a need to develop
  new tools for automated layout of EVA blocks. There are a number of problems that complicate
  the actual representation of knowledge in CAD and are probably solvable at the current level
  of cognitive science development. The problem of stereotyping and the problem of coarsening are
  interrelated and need to create hybrid models of representation. The paper deals with the problem
  of solving the problem of EVA block layout in the design of radio-electronic equipment. The purpose
  of this work is to find ways to optimize the planning of EVA block layout using a genetic
  algorithm. The relevance of the work is that the genetic algorithm can improve the quality of layout
  planning. These algorithms allow you to improve the quality and speed of layout planning. The
  scientific novelty lies in the search and analysis of effective methods for composing EVA blocks
  using genetic algorithms. The main difference from the known comparisons is in the analysis of
  new promising algorithms for composing EVA blocks. Result of work. The paper shows the disadvantages
  of traditional algorithms for searching for a suboptimal EVA plan. Descriptions of modern
  models of evolutionary and other calculations are given. Genetic algorithms have a number of
  important advantages – adaptability to a changing environment, the evolutionary approach makes
  it possible to analyze, Supplement and change the knowledge base depending on changing conditions,
  as well as quickly create optimal solutions. If you apply genetic algorithms and preprocessing
  heuristics to provide optimal initial solutions, you can achieve more productive use of
  algorithms. Known genetic algorithms converge quickly, but they lose population diversity, which
  affects the quality of the solution. To balance data, the solution is corrected using efficient operators
  or stable mutation.

• HYBRID BIOINSPIRED ALGORITHM FOR ONTOLOGIES MAPPING IN THE TASKS OF EXTRACTION AND KNOWLEDGE MANAGEMENT

  D.Y. Kravchenko, Y.A. Kravchenko, V. V. Markov

  2020-07-20

  Abstract ▼

  The article is devoted to solving the problem of mapping ontological models in the processes
  of extracting and knowledge management. The relevance and significance of this task are due to
  the need to maintain reliability and eliminate redundancy of knowledge during the integration
  (unification) of various origins structured information sources. The proximity and consistency of
  the conceptual semantics of the combined resource during the mapping is the main criterion for
  the effectiveness of the proposed solutions. The article considers the problems of choosing appropriate
  solution approaches that preserve semantics when displaying concepts. The strategy of
  choosing bio-inspired modeling is substantiated. The aspects of the effectiveness of various decentralized
  bio-inspired methods are analyzed. The reasons for the need for hybridization are identified.
  The paper proposes to solve the problem of mapping ontological models using a bio-inspired
  algorithm based on hybridization of bacterial and cuckoo search algorithms optimization mechanisms.
  The hybridization of these algorithms allowed us to combine their main advantages: a consistent
  bacterial search that provides a detailed study of local areas, and a significant number of
  the cuckoo agent during the implementation global movements of Levy flights. To evaluate the
  effectiveness of the proposed hybrid bio-inspired algorithm, a software product was developed and
  experiments were performed on the mapping of different sizes ontologies. Each concept of any
  ontology has a certain set of attributes, which is a semantic vector of attributes. The degree of the
  semantic vectors similarity for the compared concepts of displayed ontologies is a criterion for
  their integration. To improve the quality of the display process, a new encoding of solutions has
  been introduced. The quantitative estimates obtained demonstrate time savings in solving problems
  of relatively large dimension (from 500,000 ontograph vertices) of at least 13 %. The time
  complexity of the developed hybrid algorithm is O (n 2). The described studies have a high level of
  theoretical and practical significance and are directly related to the solution of classical problems
  of artificial intelligence aimed at finding hidden dependencies and patterns on a multitude of
  knowledge elements.

• IMPLICIT THREATS IDENTIFICATION BASED ON ANALYSIS OF USER ACTIVITY ON THE INTERNET SPACE

  V. V. Bova , D. Y. Zaporozhets, Y.A. Kravchenko , E. V. Kuliev , V. V. Kureichik , N. A. Lyz

  2020-10-11

  Abstract ▼

  The article is devoted to the problem of identifying implicit information threats of a user's
  search activity in the internet space based on an analysis of his activity in the course of this interaction.
  The use of knowledge stored in the Internet space for the implementation of criminal intentions
  poses a threat to the whole society. Identifying malicious intent in the users’ actions of the
  global information network is not always a trivial task. The proven technologies for analyzing the
  context of user interests fail in the case of cautious and competent actions of attackers who do not
  explicitly demonstrate the goal they are pursuing. The paper analyzes the threats associated with
  certain scenarios for the implementation of search procedures that manifest themselves in search
  activities. Criteria of inefficient and effective search scenarios estimation are described. Among
  the signs indicating the possibility of a threat, the following main ones are highlighted: avoiding
  solving the problem in aimless navigation or attractive resources, superficial search, lack of
  meaningful immersion in solving the search problem, and chaotic actions during the search.
  To determine the presence of adverse signs, a system of indicators is built. The features of an effective
  scenario for organizing a search in the Internet space are formulated, options for the presence
  of implicit threats for a similar situation are described.An approach for identification the
  described threats is presented taking into account the specified criteria for evaluating various
  scenarios of user behavior in the global information space. A machine learning algorithm has
  been developed to identify problem scenarios by comparing with key behavioral patterns. The
  software implementation of the subsystem for identifying information threats has been created,
  experimental studies have been conducted to confirm the effectiveness of the subsystem. Experimental
  studies were carried out on the basis of processing open data from social networks, as well
  as using analysis of user search activity in the university corporate information environment.