Abstract:
Query optimization, the process of producing an optimal execution plan for the problematic query, is more challenging in
such systems due to the huge search space of other plans incurred by distribution. Due to the continually updating environment of
fixed queries, the query optimizer has to frequently adjust the optimal execution plan for a query. The number of permutations in a
query grows exponentially as the number of related tables in the query grows, which is a process used to assess the cost of upgrading
searches. On the one hand, optimizing the join operator in a relational database is the most difficult and complex task. Following that,
numerous strategies have been created to address all of these concerns. The efficacy of query optimization, on the other hand, required
the use of a reinforcement learning model. Ant Colony Optimization (ACO) algorithm and Q Learning were proposed in the current
research to address this issue and improve workload delay, Optimization Time, and Cost. Q-learning techniques are compared with Ant
Colony Optimization and can be utilized to identify optimistic queries with minimal workload delay and query costs. When compared
to the Q-Learning algorithm, using a non-dominated ACO algorithm can discover optimistic queries and reduce query cost.
