DEVELOPMENT OF A SOLUTION SEARCH METHOD USING AN IMPROVED FISH SCHOOL ALGORITHM.

The object of research is decision support systems. The subject of research is the decision-making process in management problems using the fish school (FSH) algorithm, an advanced genetic algorithm and evolving artificial neural networks. A solution search method using an improved FSH algorithm is proposed. The study is based on the FSH algorithm for finding a solution on the object state. For training FSH, evolving artificial neural networks are used. The method has the following sequence of actions: - input of initial data; - processing of initial data taking into account the degree of uncertainty; - checking the fitness function of the solution found; - procedure of feeding fish agents (FA); - instinctive-collective FA swimming; - calculation of the center of school gravity; - collective voluntary FA swimming; - changing the FA swimming parameters; - training of FA knowledge bases. The originality of the proposed method lies in the arrangement of FA taking into account the uncertainty of the initial data, improved global and local search procedures taking into account the degree of noise of data about the state of the analysis object. The peculiarity of the proposed method is the use of an improved FA training procedure. The training procedure consists in learning 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 use of the method makes it possible to increase the efficiency of data processing at the level of 18-25 % 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 interest of solving national security problems. [ABSTRACT FROM AUTHOR]

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