Big data feature selection using fish and frog optimization

Big data denotes a large amount of data which includes a wide range of such methodologies like big data collection, storage, analysis, and managing the data. Every data collected in this process (homogeneous or heterogeneous considered as data), we called as big data. In this article, fish colony and their social behavior are used recently for developing an algorithm, we called as novel represented as fish swarm optimization algorithm (FSOA), which is based on the fish swarm and its behavior while search for food. The shuffled frog leaping algorithm (SFLA) is one which we introduced recently for finding near optimal solutions. The technique of Hybrid FSO‐SFLA is used here for evaluating performance in big data queries.