Clustering and Optimization Based on Hybrid Artificial Bee Colony and Differential Evolution Algorithm in Big Data

Abstract

Big data is increasingly being employed in a variety of fields because it can handle the difficulties associated with processing massive amounts of data, including business, financial transactions, medical, and more. In this paper, an approach for optimizing for searching big data is developed using Hybrid Artificial Bee Colony and Differential evolution algorithm (HABCO_DE) and finally the optimized search data is classified by using optimized, The suggested HABCO DE technique then classifies each data sample based on posterior probability of data and probability index table. Using Multi-Layer Perceptron (MLP) neural networks, a real-time, precise search target classifier was designed. Simulation results indicate that the HABCO_DE can effectively be used for data clustering.