Storing as well as retrieving the data on a specific time frame is fundamental for any application today. So an efficiently designed query permits the user to get results in the desired time and creates credibility for the corresponding application. To avoid the difficulty in query optimization, this paper proposed an improved query optimization process in big data (BD) using the ACO-GA algorithm and HDFS map-reduce. The proposed methodology consists of ‘2’ phases, namely, BD arrangement and query optimization phases. In the first phase, the input data is pre-processed by finding the hash value (HV) using the SHA-512 algorithm and the removal of repeated data using the HDFS map-reduce function. Then, features such as closed frequent pattern, support, and confidence are extracted. Next, the support and confidence are managed by using the entropy calculation. Centered on the entropy calculation, the related information is grouped by using Normalized K-Means (NKM) algorithm. In the 2nd phase, the BD queries are collected, and then the same features are extorted. Next, the optimized query is found by utilizing the ACO-GA algorithm. Finally, the similarity assessment process is performed. The experimental outcomes illustrate that the algorithm outperformed other existent algorithms.