Improvement of the optimization method based on the cat pack algorithm

Abstract

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