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The objects of the study are decision support systems. The subject of the study is the decision-making process in management problems using the Emperor Penguin Algorithm (EPA), an advanced genetic algorithm and evolving artificial neural networks. A solution search method using the improved EPA is proposed. The study is based on the EPA algorithm for finding a solution regarding the object state. Evolving artificial neural networks are used to train EPA, and an advanced genetic algorithm is used to select the best EPA. The method has the following sequence of actions: – input of initial data; – setting agents on the search plane; – numbering EPA in the flock; – setting the initial velocity of the EPA and thermal radiation of each EPA; – calculation of the position of each EPA on the total search area and its cost; – approach (attraction) of the EPA to another EPA; – changing in the trajectory of EPA movement; – selection of the best individuals from the EPA flock; – ranking the obtained solutions and sorting them; – training EPA knowledge bases; – determining the amount of necessary computing resources for an intelligent decision support system. The originality of the proposed method lies in setting EPA taking into account the uncertainty of the initial data, improved global and local search procedures taking into account the noise degree of data on the state of the analysis object. The method makes it possible to increase the efficiency of data processing at the level of 13–17 % due to the use of additional improved procedures. The proposed method should be used to solve the problems of evaluating complex and dynamic processes in the interests of solving national security problems [ABSTRACT FROM AUTHOR]

The object of the research is decision support systems. The subject of the research is the decision-making process in management problems using the monkey algorithm and evolving artificial neural networks. A solution search method using an improved monkey algorithm is proposed. The research is based on the monkey algorithm – for finding a solution regarding the state of an object. For training monkey agents (MA), evolving artificial neural networks are used. The method has the following sequence of steps: – input of initial data; – processing of initial data taking into account the degree of uncertainty; – a search vector is generated for each MA, taking into account the degree of uncertainty; – determination of the initial speed of MA movement; – calculation of the fitness function of the MA solution; – calculation of the height of MA movement; – verification of fulfillment of local jump conditions; – generation of local search plane coordinates; – calculation of the fitness function of the MA solution; – generation of global search plane coordinates; – search distribution among the MA flock; – changing the speed of MA movement; – checking the permissible value of the obtained solution regarding the object state; – training of MA knowledge bases. The originality of the proposed method lies in the arrangement of MA taking into account the uncertainty of the initial data, improved procedures of global and local search taking into account the degree of noise of data about the state of the analysis object. A feature of the proposed method is the use of an improved MA training procedure. The training procedure consists in learning the parameters and architecture of individual elements and the architecture of the artificial neural network as a whole. The method makes it possible to increase the efficiency of data processing at the level of 23–28 % due to the use of additional improved procedures [ABSTRACT FROM AUTHOR]

Artificial intelligence technologies are actively used to solve both general and highly specialized tasks. In the process of assessing (identifying) the condition of complex and heterogeneous objects, there is a high degree of a priori uncertainty regarding their condition and a small amount of initial data describing them. The trends of armed conflicts of the last decades and the regularities of the development of information systems, convincingly indicate the need to change approaches to the collection of information from various sources and their analysis. There is a constant transformation of the forms of information presentation and the order of storage and access to various types of data. The problem of integrating disparate sources of information collection into a single information space is also not fully resolved. That is why the issue of improving the efficiency of assessing the state of complex and heterogeneous dynamic objects is an important and urgent issue. The objects of research are heterogeneous dynamic objects. The subject of the research is the identification of the state of heterogeneous dynamic objects. In the research, the method of identifying the state of heterogeneous dynamic objects was developed. The novelty of the proposed method consists in: – taking into account the degree of uncertainty about the state of a heterogeneous dynamic object; – taking into account the degree of data noise as a result of distortion of data characterizing the state of a heterogeneous dynamic object; – reducing computing costs while assessing the state of heterogeneous dynamic objects; – the possibility of performing calculations with source data that are different in nature and units of measurement. It is advisable to implement the mentioned method in specialized software, which is used to analyze the state of complex technical systems and make decisions. [ABSTRACT FROM AUTHOR]

A method of complex analysis and multidimensional forecasting of the state of intelligence objects is proposed to increase the accuracy of their state assessment. The object of research is decision support systems. The subject of research is the process of decision-making in management problems using artificial intelligence methods. The hypothesis of research is to increase the efficiency of decision-making with a given assessment reliability. The proposed method is based on a combination of fuzzy cognitive and temporal models, an advanced cat swarm optimization algorithm and evolving artificial neural networks. The method has the following sequence of actions: – input of initial data; – processing of initial data taking into account uncertainty about the state of heterogeneous intelligence objects; – construction of a fuzzy temporal ontological model of heterogeneous intelligence objects; – conclusion on the state of heterogeneous intelligence objects; – correction of the fuzzy temporal ontological model; – building a fuzzy relational temporal cognitive model of heterogeneous intelligence objects and forecasting the state of the intelligence object; – training knowledge bases on heterogeneous intelligence objects. The training procedure consists in learning the synaptic weights of the artificial neural network, the type and parameters of the membership function, as well as the architecture of individual elements and the architecture of the artificial neural network as a whole. The method makes it possible to increase the efficiency of data processing at the level of 18–25 % by using additional improved procedures. The proposed method should be used to solve the problems of evaluating complex and dynamic heterogeneous intelligence objects, characterized by a high degree of complexity. [ABSTRACT FROM AUTHOR]

The problem that is being solved in the research is to increase the efficiency of decision-making in management tasks while ensuring the given reliability, regardless of the hierarchical nature of the object. The object of research is the decision-making support systems. The subject of the research is the decision-making process in management tasks using an improved cat flock algorithm. The research hypothesis is the possibility of increasing the efficiency of decision-making with a given assessment reliability. In the course of the research, an improved method of parametric optimization based on the improved algorithm of the cat flock was proposed. In the course of the research, the general provisions of the theory of artificial intelligence were used to solve the problem of analyzing the state of objects and subsequent parametric management in intelligent decision-making support systems. The essence of the method improvement lies in the use of the following procedures, which improve basic procedures of the cat flock algorithm, namely, search and chase: – training of individuals of a cat flock with the help of evolving artificial neural networks; – taking into account the type of uncertainty of the initial data while constructing the metric of the path of the cat flock, which reduces the time of searching for the optimal solution; – searching for a solution in several directions using individuals from the cat flock, which reduces the time of searching for the optimal solution; – initial display of individuals from the cat flock not randomly; – additional consideration of the chase parameter, which limits the chase area, which allows to take into account the priority of the search; – the ability to determine the need to involve additional network hardware resources. An example of the use of the proposed method is presented on the example of assessing the state of the operational situation of a group of troops (forces). The specified example showed a 17–23 % increase in the efficiency of data processing due to the use of additional improved procedures. [ABSTRACT FROM AUTHOR]

The problem that is solved in the study is to increase the efficiency of decision-making regarding the state of the control object while ensuring a given reliability, regardless of the object’s hierarchy. The object of the study is decision support systems. The subject of the study is the process of assessment and parametric control of the state of the control object using the firefly algorithm. The hypothesis of the study is an increase in the efficiency of assessing the state of the control object with a given reliability. In the course of the study, an improved method of parametric control of the control object based on the improved firefly algorithm was proposed. General provisions of artificial intelligence theory were used for solving the problem of object state analysis and subsequent parametric control in intelligent decision support systems. The essence of improvement is to use the following procedures: − taking into account the type of uncertainty about the state of the control object (complete uncertainty, partial uncertainty and complete awareness); − taking into account the noise of data on the state of the control object. Data noise refers to the degree of information distortion created by the enemy’s electronic and cyber warfare; − using the improved firefly algorithm to find the path metric while assessing the state of the control object; − deep learning of the synthesized ants using evolving artificial neural networks. The application of the proposed method is presented on the example of assessing the state of the operational situation of a group of troops (forces). The specified example showed a 17–20 % increase in the efficiency of data processing using additional improved procedures. [ABSTRACT FROM AUTHOR]

The most characteristic features of the construction of communication systems of groups of troops (forces) during the conduct of hostilities (operations) are a high level of a priori uncertainty regarding the operational situation and a small amount of initial data for communication planning. In such conditions, it is important to correctly choose the apparatus for evaluating the made management decisions, which will allow the officials of the points of the control system of the communication system of the groups of troops (forces) to be confident in the made decisions. That is why the issue of increasing the effectiveness of the management of forces and devices of communication of groups of troops (forces) during operations is an important and urgent issue. The object of the research is the communication system of the group of troops (forces). The subject of the research is the effectiveness of the communication system of the grouping of troops (forces) in accordance with the purpose of the operation. The research developed a method for managing forces and devices of communication using methods of resource optimization in the operations of troops (forces). The novelty of the proposed method consists in taking into account the type of uncertainty regarding the operational situation in the operational space. Also, taking into account the number of members of the grouping (consumers of communication services) of groups of troops (forces) in operations. The novelty of the developed method consists in taking into account the duration of the operation (fighting) and the calculation of the labor costs necessary to meet the needs of the communication services of groups of troops (forces) while planning measures for the distribution and use of forces and devices of communication. The specified method is proposed to be implemented: – in planning documents during planning the deployment and operation of forces and devices of communication; – in the software during operational management of the communication system of troop groups. [ABSTRACT FROM AUTHOR]

The object of the research is a special-purpose communication system. The relevance of the research lies in the need for complex management of resources of special-purpose communication systems. The resources of the special-purpose communication system are defined as: spatial, temporal, frequency and hardware resources. Destabilizing factors include: intentional interference; denial-of-service cyber attacks and fire damage to individual elements of the special-purpose communication system. The method of complex management of resources of special-purpose communication systems was improved. The difference between the proposed method and the known ones is that the specified method contains improved procedures: – determination of the impact of destabilizing factors on the special-purpose communication system; – description of special-purpose communication systems of various architectures; – determination of the rational route of information transmission and operation mode of communication devices in the general special-purpose communication system; – consideration of uncertainty about the state of the special-purpose communication system; – determination of the number of necessary forces and means of communication, which must be increased for the full functioning of the special communication system. The improved method provides a gain of 20–26 % compared to classical approaches to the management of resources of special-purpose communication systems. The improved method can be used at the control points of the communication system of groups of troops (forces) while planning the organization of communication and at the stage of operational management of the communication system. [ABSTRACT FROM AUTHOR]

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To implement the operation of a radio technical complex according to its technical condition, it is necessary to jointly evaluate its reliability and residual life indices with required accuracy and reliability and minimization of the scope of special tests. The known methods are focused on separate solutions to the problems of estimating these indices as applied to the regulated strategy. To solve this problem, general provisions have been developed for estimating the indices of residual life of the radio technical complex including the accepted assumptions and limitations for developing the method, the estimated indices, and criteria of limiting state. The developed experiment-calculated method is a set of mathematical models of change of the reliability indices of a radio technical complex depending on calendar duration of operation or total operating time and analytical models of estimating the indices of its residual life. The mathematical models of change of mean time between failures, the probability of failure-free switching, and the parameter of the flow of failures of the radio technical complex depending on calendar duration of operation or the total operating time were presented in a form of regressive dependences. Analytical models of estimating the residual life indices are ratios for calculating the "average residual service life (resource)" according to the technical and economic criterion using regression-time dependences of the reliability indices. The developed experimentcalculated method can be used to estimate the indices of residual life of the radio technical complex with acceptable accuracy (no more than 2 quarters) and reliability (no worse than 0.8). In this case, the duration of the intervals of predicting the reliability indices should be 0.5 to 1 year and the corresponding observation intervals should be more than 1 year [ABSTRACT FROM AUTHOR]