Query optimization in relational databases is a costly process, and the number of different permutations of link operations for a query grows exponentially as the number of tables in the query increases. Current query optimization techniques are unsuitable for use in cases where the number of database tables and the number of rows in each table are large. On the other hand, the key to the success of a database system is the efficiency of its query model. Therefore, this research proposes a method for optimizing query execution by rearranging query structures.

Due to the increasing importance of reducing the execution time of the optimal design in the connection operation with a large number of tables and minimizing the execution time of the algorithm to find the optimal design and not getting stuck in local optimizations, the use of new metaheuristics and efficient algorithms has received much attention. In this research, we used the Genetic algorithm and the Moth Flame Optimization algorithm. Finally, we test our proposed method on the employee database with 200 random queries. In comparison to random and GA runs, our method gets 22.5% and 4.2% better response time, respectively.