РЕШЕНИЕ ЗАДАЧИ ИНТЕЛЛЕКТУАЛЬНОГО АНАЛИЗА ДАННЫХ НА ОСНОВЕ БИОИНСПИРИРОВАННОГО АЛГОРИТМА

Abstract

The article discusses a bioinspired algorithm for solving the problems of intellectual analysis.The integration of bioinspired algorithms for solving data mining problems is a promisingarea of research. As a bioinspired algorithm, an algorithm based on the adaptive behavior of anant colony is considered. The ant colony algorithm allows for a high-quality search for promisingsolutions to obtain optimal and quasi-optimal solutions. The algorithm has the ability to search forsuitable logical conditions. The ant colony algorithm is based on the example of the behavior ofliving ants in nature. Ants are able to find the shortest solution by adapting to changes in the environment.The authors proposed a modified ant colony algorithm for solving the problem of datamining. The clustering problem was chosen as the task of data mining. Clustering is a combiningof similar objects into groups, is one of the fundamental tasks in the field of data analysis andData Mining. The list of application areas where it is applied is wide: image segmentation, marketing,anti-fraud, forecasting, text analysis and many others. The solution to this problem is of particular relevance in the context of the constantly growing volume of generated, transmitted andprocessed data. Classical clustering methods are optimized by combining with the proposedbioinspired optimization algorithm - the ant algorithm. The proposed method is a model in whichants are represented as agents that randomly move in the solution space with some restrictions(for example, obstacles in their path). To determine the effectiveness of the developed modified antalgorithm (ALA) with the clustering algorithm, the authors carried out a series of computationalexperiments. For comparison, we took the genetic algorithm, the monkey algorithm and the wolfalgorithm. The simulation results prove that the clustering-based ant algorithm gives better resultsthan other proposed algorithms.