A Combined Bioinspired Algorithm for Big Data Processing

The paper considers the development of a biologically plausible algorithm for solving big data processing problems. The intellectual data analysis is an important part of the big data technologies. In terms of big data mining, it is reasonable to perform intelligent data analysis to retrieve the important parameters and find regularities. The authors analyze the classification task as one of the most important problems in data mining and the popular solutions for its solving, including bioinspired algorithms as one of the most effective approaches. The paper suggests a new concept aiming to provide the population diversity to improve the effectiveness of biologically likely algorithms for the problems of large dimensions. The bioinspired algorithm used in the paper is represented by an artificial bee colony (ABC) algorithm. The authors offer a combined algorithm based on the sequential work of evolutionary adaptation and swarm intelligence. The experimental research was conducted to prove the effectiveness of the algorithm and estimate its working time. The results are demonstrated in the paper.