Development of the solution search method using the population algorithm of global search optimization

Abstract

The research objects are decision making support systems. subject research is a decision making process in management tasks using bio-inspired algorithms. A method of finding solutions using the population algorithm of global search optimization is proposed. Joint use of invasive weed algorithm is proposed, genetic algorithm and evolving artificial neural networks are improved. The method has the following sequence of actions: ‒ an input of initial data; ‒ processing of initial data taking into account the degree of uncertainty; ‒ formation of the optimization vector; ‒ creation of descendant vectors; ‒ ordering of vectors in descending order; ‒ reducing the dimensionality of the feature space; ‒ teaching knowledge bases. The peculiarity of the proposed method lies in the placement of agents-weeds, taking into account the uncertainty of the initial data, improved procedures for reducing the space of signs about the analysis object state. Training of synaptic weights of an artificial neural network, type and parameters of the membership function and the architecture of individual elements, and the architecture of an artificial neural network as a whole is carried out. The proposed method was simulated in the MathСad 14 software environment. The task to be solved during the simulation was to determine the route of the ships in the operational zones of the Black and Azov seas in the conditions of hybrid actions of the enemy. The use of the method makes it possible to increase the efficiency of data processing at the level of 21–27 % 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 interests of solving national security problems