Join Query Optimization Using Lion Optimization Algorithm And Hybrid Dqn-Loa And Ddqn-Loa Approaches

Join query optimization is a critical task in database management systems as it directly impacts the performance and efficiency of query execution. The goal of join query optimization is to determine an optimal execution plan for a given query, considering factors such as cost, resource utilization, and query response time. In recent years, metaheuristic algorithms have shown promise in solving optimization problems, including query optimization. One such algorithm is the Lion Optimization Algorithm (LOA), inspired by the hunting behavior of lions in nature. LOA utilizes a population-based search approach and has demonstrated effectiveness in solving various optimization problems. On the other hand, deep reinforcement learning techniques, such as the Deep Q-Network (DQN) and Double DQN (DDQN), have gained significant attention in solving complex problems. DQN and DDQN leverage neural networks and reinforcement learning principles to optimize decision-making processes. This research paper aims to analyze the application of the Lion Optimization Algorithm (LOA) and hybrid DQN-LOA and DDQN-LOA approaches for join query optimization. By integrating LOA with deep reinforcement learning techniques, we aim to improve the efficiency and effectiveness of query optimization.