QIACO: A Quantum Dynamic Cost Ant System for Query Optimization in Distributed Database

Query optimization is considered as the most significant part in a model of distributed database. The optimizer tries to find an optimal join order, which reduces query execution cost. Several factors may affect cost execution, including number relations, communication costs, resources, and access large data sets. success processed depends heavily on search methodology that implemented by optimizer. processing NP-hard problem many researchers are focusing this problem. Researches trying appropriate algorithm seek ideal solution especially when size database increases. In case queries, classical heuristic methods such ant colony genetic can't cover all space lead falling local minimum. paper, quantum inspired (QIACO), one hybrid strategy probabilistic algorithms, utilized improve model. ability computing diversify leads space, helps selecting best trail thus improves slow convergence speed avoid into optimum. Using strategy, aims order minimizes total time. Experimental results show proposed faster with better goodness than classic for same ants used.