Development of a method for assessing the state of dynamic objects using a population algorithm

Abstract

The object of the study is complex dynamic objects with a hierarchical structure. A method for assessing the state of dynamic objects using a population algorithm is proposed. The study is based on the snake optimization algorithm for finding a solution to the state of dynamic objects with a hierarchical structure. For training snake agents (SA), evolving artificial neural networks are used. The originality of the method lies in using additional advanced procedures that allow you: – to determine the initial position of SA, taking into account the type of uncertainty by using a correction factor for the degree of awareness of the state of the initial situation in relation to the object of analysis; – to take into account the initial velocity of each SA, which allows studying complex functions; – to ensure the universality of SA food location search strategies, which allows classifying the type of data to be processed; – to adjust the SA velocity by adjusting the ambient temperature, which allows priorizing the search for a solution in a certain plane; – to explore the solution spaces of functions described by non-typical functions, using exploitation mode procedures; – to flexibly adjust the transition from the SA fighting mode to the mating mode using the food saturation coefficient; – to replace individuals unsuitable for search using the SA fertility rate; – to conduct a simultaneous search for a solution in different directions, by changing the ambient temperature and adjusting the food saturation coefficient. Modeling showed a 13–19 % increase in data processing efficiency by using additional improved procedures.