A Hierarchical Optimization Approach for Big data Pre-processing and Search Optimization using HMGA-ACO

Abstract

Because of the enormous potential associated with big data, a new study area has emerged that has swiftly p iqued the attention of researchers from a wide range of industries. It is thought that Big Data, in contrast to conventional databases, which are geared for the rapid access to and summary of structured data and well-defined searches, will serve as a raw material for the generation of new knowledge. Using search and optimization issues as an example, the complexity imposed by large search spaces, which are dominated by the number of search words and the doma in of each variable has been investigated. The efficiency and efficacy of search are reduced when the search domain is enormous, even infinite. A complicated structure of restrictions further enhances the complexity of search by making the search space more irregular. The authors present a unique Hierarchical Manipulated Genetic Algorithm with Ant Colony Optimization (HM GA-ACO) with data pre-processing in order to address the aforementioned concerns. This algorithm has the ability to optimize the data and retrieve the data with more accuracy and precision. When pre-processing is used, the suggested hierarchical optimization may assist in increasing the speed of search, while also decreasing the effort required for search.