Authors : Saeed Sharif and Hamid Reza Ghaffary
Abstract: Optimizing database queries is one of the problems with research issues. Comprehensive search methods such as dynamic programming is suitable for queries with a few relations but by increasing the number of existing relations in the query, due to the need to use a lot of memory and processing, use of these methods will not be suitable, so we have to use the accidental and evolutionary methods. Using evolutionary methods due to their performance and strengthen, has become a suitable research area in the field of query optimization. In this study, a parallel hybrid evolutionary algorithm is proposed for solving order optimization problem of running join operators in the database queries. The algorithm uses two methods of genetic algorithms and learning automata for searching in the problem states space at the same time. In this study, it is shown that by using a synchronously Particle Swarm Optimization algorithm (PSO) in parallel with the genetics crossover operator in the search process, the speed of receiving answer increases and it is prevented from algorithms to get stuck in the local minimums. The practical results of this study show that the hybrid algorithm shows superior to methods based on other algorithms.
Saeed Sharif and Hamid Reza Ghaffary, 2019. Mixing Evolutionary Algorithms by Data Mining Approach for Query Optimization. International Journal of Signal System Control and Engineering Application, 12: 128-136.