Progress in Computing Applications Rama Satish K. V, Kavya N. P 7 1 2018 http://www.dline.info/pca/fulltext/v7n1/pcav7n1_2.pdf The huge potential associated with big data has led to an emerging research field that has quickly attracted tremendous interest from diverse sectors. Unlike traditional databases, optimized for fast access and summarization of structured data and well defined queries, Big Data is believed to serve as a raw material for the creation of new knowledge. We look at the complexity placed by big search spaces, dominated by the number of variables and domain of each variable, in search and optimization problems. While a large, even infinite, search domain impairs the effectiveness and efficiency of search, a complex structure of constraints further increases the difficulty in that the search space becomes highly irregular. In order to overcome the above issues we propose a novel Hierarchical Manipulated Genetic Algorithm with Ant Colony Optimization (HMGA-ACO) with data pre-processing which has the potential to optimize the data and retrieve the data with more accuracy and precision. The proposed hierarchical optimization can help to boost the speed of search, and the effort of search is reduced with the utilization of pre-processing.