Research Progress of Intelligent Optimization Algorithm in Big Data Background

Abstract

Big data is the inevitable outcome of the rapid development of modern information technology, effective analysis and processing of big data will not only bring great economic value, but will also promote social development. Under the big data environment, the data scale, speed of emergence and its difficulty make optimizing issues very complex. In recent decades, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), Artificial Fish School Algorithm, Bacteria Foraging Optimization Algorithm (BFOA), artificial neural networks (ANNs) and other multi-population intelligent algorithms appeared. In this paper, several typical intelligent optimization algorithms are introduced, including genetic algorithm, particle swarm optimization algorithm, ant colony algorithm, artificial fish swarm algorithm and bacterial foraging algorithm. The basic principles of five algorithms are described respectively, along with the direction of improvement and feasible applications.