Improvement of the method of parametric control of the state of the control object based on the improved firefly algorithm
Keywords:control object, firefly algorithm, data noise, evaluation efficiency, reliability of decisions
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
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Copyright (c) 2022 Yurii Zhuravskyi, Oleksii Nalapko, Roman Vozniak, Andrii Veretnov, Oleh Shknai, Anton Nikitenko, Oleksandr Pechorin, Yurii Mikhieiev, Vitalii Shevchuk, Mykola Zaitsev
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