Development of a solution search method using an advanced flying squirrel algorithm

Abstract

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