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CLASSIFICATION AND ANALYSIS OF EVOLUTIONARY METHODS OF EVA BLOCK LAYOUT
Y.V. Danilchenko, V.I. Danilchenko, V. M. Kureichik2020-07-20Abstract ▼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. -
DESCRIPTION OF GRAPHS WITH ASSOCIATIVE OPERATIONS IN SET@L PROGRAMMING LANGUAGE
I. I. Levin , I. V. Pisarenko, D. V. Mikhailov , A. I. Dordopulo2020-10-11Abstract ▼Usually, an information graph with associative operations has a sequential (“head/tail”) or
parallel (“half-splitting”) topology with invariable quantity of operational vertices. If computational
resource is insufficient for the implementation of all vertices, the reduction transformations
of graphs with basic topologies do not allow for the creation of an efficient resource-independent
program. In fact, the “half-splitting” variant is characterized by irregular connections between
iterations, and the “head/tail” structure has an increased data duty cycle in the reduced form.
In this paper, we propose to transform the topology of a graph with associative operations into a
combined variant with sequential and parallel fragments of calculations. The resultant combined
topology depends on computational resource of a parallel computer system, and such transformation
provides the improvement of specific performance for the reduced computing structure.
The considered topology contains isomorphic subgraphs with the “half-splitting” topology, which
include the maximal number of hardwarily implemented operational vertices, but the processing of
intermediate data is performed using the “head/tail” principle. The computing structure for the
combined topology has minimal latency and includes one basic subgraph and one vertex with
feedback. This vertex is obtained as a result of the “head/tail” block reduction. We develop an
algorithm for the conversion of the initial sequential graph to various combined topologies or to
the limiting case of the “half-splitting” topology with regard to available hardware resource.
Within traditional methods of parallel programming, it is possible to describe the variety of topologies
only as a set of separated subprograms. To create an efficient resource-independent program,
we propose the application of the Set@l programming language. We describe the
“head/tail” and “half-splitting” principles as the attributes of set processing methods in Set@l.
Resource-independent program uses these types and parallelism attributes for the modification of
topology and further reduction of performance in the corresponding aspects. -
AUTOMATED STRUCTURAL-PARAMETRIC SYNTHESIS OF A STEPSED DIRECTIONAL RESPONDER ON CONNECTED LINES BASED ON A GENETIC ALGORITHM
Y. V. Danilchenko , V.I. Danilchenko, V. M. Kureichik2020-11-22Abstract ▼An automated approach to the structural-parametric synthesis of a stepped directional coupler
on connected lines based on a genetic algorithm (GA) is described, which makes it possible to
create an algorithmic environment in the field of genetic search for solving NP complete problems,
in particular, the structural-parametric synthesis of a stepped directional coupler on connected
lines. The purpose of this work is to find ways of structural-parametric synthesis of a stepped directional
coupler on coupled lines based on the bionspiration theory. The scientific novelty lies in
the development of a modified genetic algorithm for automated structural-parametric synthesis ofa stepped directional coupler on connected lines. The problem statement in this work is as follows:
to optimize the synthesis of passive and active microwave circuits by using a modified GA. A fundamental
difference from the known approaches in the use of new modified genetic structures in
automated structural-parametric synthesis, in addition, a new method for calculating a stepped
directional coupler on connected lines based on a modified GA is righteous in the work. Thus, the
problem of creating methods, algorithms and software for automated structural synthesis of microwave
modules is currently of particular relevance. Its solution will improve the quality characteristics
of the designed devices, reduce design time and costs, and reduce the requirements for
developer qualifications. -
METHOD FOR DETECTING FEATURE POINTS OF AN IMAGE USING A SIGN REPRESENTATIONS
A. N. Karkishchenko, V. B. Mnukhin2020-11-22Abstract ▼The aim of the study is to develop a method for detecting feature points of a digital image
that is stable with respect to a certain class of brightness transformations. The need for such a
method is due to the needs of detecting feature points of images in video surveillance systems and
face recognition, often working in a changing light environment. A feature of the proposed method
that distinguishes it from a number of well-known approaches to the problem of distinguishing
characteristic points is the use of the so-called sign representation of images. In contrast to the
usual defining of a digital image by a discrete brightness function, with a sign representation, the
image is set in the form of an oriented graph corresponding to the binary relation of the increase
in brightness on a set of pixels. Thus, the sign representation determines not a single image, but a
set of images, the brightness functions of which are connected by strictly monotonic brightness
transformations. It is this property of the sign representation that determines its effectiveness for
solving the problems caused by the goal set above. A feature of the method under consideration is
a special approach to the interpretation of the characteristic points of the image. This concept in
image processing theory is not strictly defined; we can say that the characteristic point is characterized
by increased "complexity" of the image structure in its vicinity. Since the sign representation
of the image can be represented in the form of a directed graph, in this paper, to evaluate the
complexity measure of the local neighborhood of its vertices, it is proposed to use the ranking
method known in the spectral theory of graphs based on the Perron-Frobenius theorem. Its essence
lies in the fact that the value of the component of the so-called Perron eigenvector of the
adjacency matrix of this graph acts as a measure of the complexity of the vertex. To conduct experimental
studies of the proposed approach, a set of programs was developed, the results of
which confirm the efficiency of the method and demonstrate that with its help it is possible to obtain
results close to the expected ones on model examples. The paper also offers a number of recommendations
on the use of this method. -
CONVERTING SOME TYPES OF SEQUENTIAL INFORMATION GRAPHS INTO PARALLEL-PIPELINE FORM
D.V. Mikhailov2021-02-25Abstract ▼Many digital signal processing tasks can be represented in the form of information
graphs. Reconfigurable computing systems based on FPGAs can have a structure that directly
corresponds to the information graph of the problem being solved. The construction of the task
graph and the subsequent creation of the computational structure can take a significant amount
of time when performed manually. In this regard, it becomes necessary to create algorithms for
transforming information graphs that can be performed automatically. The article proposes
algorithms for transforming homogeneous graphs containing associative operations and mixed
graphs containing two types of operations, one of which is distributive with respect to the other.
Transformations of graphs of the first type (consisting of operations of the same type) are reduced
to the transition from a sequential form of a graph to a pyramidal form to speed up the
execution of all graph operations. If the available amount of equipment is not enough to impl ement
all operations of the graph, a transformation is applied that splits the original graph into
isomorphic subgraphs. The size of the subgraph depends on the available computing resources.
In this case, the computational structure will correspond to such a subgraph. Transformations
of graphs of the second type (consisting of operations of two types, some of which are distributive
with respect to others) are reduced to dividing the graph into subgraphs containing operations
of the same type, connected in a special way. After that, these subgraphs can be converted
into a pyramid shape to speed up the execution of all graph operations. In this case, the number
of vertices with distributive operations can increase significantly, and therefore it may be necessary
to reduce their number. It follows that when transforming graphs of the second type, it is
necessary to choose a specific form to which the graph will be reduced, based on the ratio of its
size and the available computing resource. Thus, the proposed algorithms for transforming
information graphs of various types can be effectively used in the development of computational
structures based on FPGAs -
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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AUTOMATED STRUCTURAL-PARAMETRIC SYNTHESIS OF A STEPSED DIRECTIONAL RESPONDER ON CONNECTED LINES BASED ON A GENETIC ALGORITHM
Y. V. Danilchenko , V.I. Danilchenko, V. M. Kureichik2021-07-18Abstract ▼All major manufacturers go to a decrease in the dimensions of modern microelectronic devices.
This leads to the transition to new standards for designing and manufacturing SBSS.
The well-known automated design algorithms are not fully able to implement new requirements
when designing a SBI. In this regard, when solving the tasks of design design, there is a need todevelop new methods for solving this class task. One of these techniques can be a hybrid multidimensional
search system based on a genetic algorithm (GA). An automated approach to the design
of the SB based on a genetic algorithm is described, which makes it possible to create an algorithmic
medium in the field of multidimensional genetic search to solve the NP full tasks, in particular
the placement of the VSA elements. The purpose of this work is to find ways to place the elements
of the SBI based on the genetic algorithm. The scientific novelty is to develop a modified
multidimensional genetic algorithm for automated design of super-high integrated circuits. The
formulation of the problem in this paper is as follows: optimize the placement of the ELEMENTS
of the SBI by using, multidimensional modified hectares. The practical value of the work is to create
a subsystem that allows you to use the developed multidimensional architecture, methods and
algorithms to effectively solve the tasks of the design design of the SDI, as well as conduct a comparative
analysis with existing analogues. The fundamental difference from the well-known approaches
in the application of new multidimensional genetic structures in the automated design of
the SBI, in addition, the modified genetic algorithm was righteous. The results of the computational
experiment showed the advantages of a multidimensional approach to solving the tasks of placing
the Elements of the SBI compared to existing analogues. Thus, the problem of creating methods,
algorithms and software for the automated placement of the SBS elements is currently of particular
relevance. Its solution will improve the qualitative characteristics of the designable devices,
will reduce the timing and costs of design. -
METAHEURISTIC OPTIMIZATION METHOD BASED ON THE STEM CELL BEHAVIOR MODEL
Y. V. Danilchenko , V.I. Danilchenko, V.M. Kureichik2022-05-26Abstract ▼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.
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