Join queries optimization in the distributed databases using a hybrid multi-objective algorithm

In the distributed database systems, the relations needed by a query can be kept in several locations. This process significantly increases potential corresponding Query Execution Plans (QEP's) for a user query. Henceforth, in addition to the expense of local computing, the charge of transferring data between different cloud sites should also be considered. It does not sound logical to investigate all potential query plans in a high setting like this. The best query plan (regarding cost) must be generated for processing a given query. A new hybrid multi-objective genetic and bat algorithm, a Multi-Objective Genetic Algorithm with BAT (MOGABAT), is used in the present article to produce the best query plans. The functionality comparison is made on different join graph structures, among MOGABAT, Multi-Objective BAT (MOBAT), and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The obtained results have shown that the quality of generated query plans is enhanced for the join graph structures. Nevertheless, more execution time is needed.