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The object of the study is complex dynamic objects. The subject of the study is the decision-making process in the problems of managing complex dynamic objects. A method of assessing the state of dynamic objects using a combined swarm algorithm is proposed. The research is based on a combined swarm algorithm - for finding a solution to the state of dynamic objects with a hierarchical structure. To train the individuals of the combined swarm algorithm (CSA), evolving artificial neural networks are used, and to select the best in the combined swarm algorithm, an improved genetic algorithm is used. The originality of the method is: – in taking into account the type of uncertainty and noise of data during the operation of the combined swarm algorithm due to the use of appropriate correction factors; – in the implementation of adaptive strategies for the search for food sources due to setting appropriate search priorities; – in taking into account the presence of a predator while choosing food sources by the flock agents of the combined swarm algorithm, which allows excluding unwanted search areas; – in the additional consideration of the available computing resources of the state analysis system of complex dynamic objects while determining the maximum permissible parameters of the combined swarm algorithm; – in the possibility of changing the search area and speed of movement by separate individuals of the flock of the combined swarm algorithm; – in determining the best individuals of the flock of the combined swarm algorithm using an improved genetic algorithm; – in training knowledge bases, carried out by training the synaptic weights of the artificial neural network, the type and parameters of the membership function, 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 14–20 % by using additional improved procedures. The proposed method should be used to solve problems of evaluating complex dynamic objects. [ABSTRACT FROM AUTHOR]

The object of the study is decision support systems. The subject of the study is the decision-making process in management problems using the flying squirrel algorithm (FSA), an advanced genetic algorithm and evolving artificial neural networks. A solution search method using an advanced FSA is proposed. The study is based on the FSA algorithm for finding a solution regarding the state of an object. Evolving artificial neural networks are used to train FSA, and an advanced genetic algorithm is used to select the best FSA. The method has the following sequence of actions. Input of initial data and setting agents on the search plane take place. After that, numbering FSA in the flock and setting the initial fitness function are carried out. Then, the quality of food in the FSA search area is determined, and the classification of trees (food sources) for FSA is carried out. The next step is the creation of new locations by FSA gliding, formation of the FSA action algorithm in the presence of a predator. After that, the FSA seasonal monitoring conditions are checked, the stop criterion is checked, and new FSA positions are generated taking into account the degree of data noise. The originality of the proposed method lies in setting FSA taking into account the uncertainty of the initial data, advanced 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 21–25 % due to the use of additional advanced 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]