Improving the method for increasing the efficiency of decision-making based on bio-inspired algorithms
Keywords:uncertainty, monitoring object, data noise, assessment efficiency, reliability of decisions
The problem that is solved in the research is to increase the efficiency of assessing the state of the monitoring object while ensuring the given reliability, regardless of the hierarchy of the monitoring object. The object of research is decision support systems. The subject of the research is the process of evaluating the monitoring object using bio-inspired algorithms. The hypothesis of the research is the need to increase the efficiency of the assessment of the state of the monitoring object with the given reliability. In the course of the research, an improved method of increasing the efficiency of decision-making based on bio-inspired algorithms was proposed. General provisions of artificial intelligence theory were used to solve the problem of analyzing the object state 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 monitoring object (full uncertainty, partial uncertainty and full awareness);
− taking into account the degree of noise in the data on the state of the monitoring object. Noise refers to the degree of information distortion created by the enemy’s means of electronic and cyber warfare;
− using the ant colony optimization algorithm and the genetic algorithm to find the path metric while assessing the state of the monitoring object;
− deep learning of synthesized ants using evolving artificial neural networks.
An example of using the proposed method in assessing the state of the operational situation of a group of troops (forces) is presented. The specified example showed a 15−22 % increase in the efficiency of data processing using additional improved procedures
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Copyright (c) 2022 Mykhailo Koval, Oleg Sova, Andrii Shyshatskyi, Yurii Artabaiev, Nataliia Garashchuk, Yurii Yivzhenko, Yuriy Luscshay, Liudmyla Dovhopoliuk, Oles Haidenko, Mykola Dorofeev
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