Development of a solution search method using the improved emperor penguin algorithm

Abstract

The objects of the study are decision support systems. The subject of the study is the decision-making process in management problems using the Emperor Penguin Algorithm (EPA), an advanced genetic algorithm and evolving artificial neural networks. A solution search method using the improved EPA is proposed. The study is based on the EPA algorithm for finding a solution regarding the object state. Evolving artificial neural networks are used to train EPA, and an advanced genetic algorithm is used to select the best EPA. The method has the following sequence of actions: – input of initial data; – setting agents on the search plane; – numbering EPA in the flock; – setting the initial velocity of the EPA and thermal radiation of each EPA; – calculation of the position of each EPA on the total search area and its cost; – approach (attraction) of the EPA to another EPA; – changing in the trajectory of EPA movement; – selection of the best individuals from the EPA flock; – ranking the obtained solutions and sorting them; – training EPA knowledge bases; – determining the amount of necessary computing resources for an intelligent decision support system. The originality of the proposed method lies in setting EPA taking into account the uncertainty of the initial data, improved 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 13–17 % 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