The Multi-join Query Optimization for Smart Grid Data

As the worldwide smart grid development, the high volume of data is generated by the smart grid devices. Database system, which stores large amounts of smart grid data is facing the growing amount of data storage and query requirements of increasingly complex. Meanwhile, smart grid has high requirements for data processing speed which makes traditional query strategies show many deficiencies. A new hybrid intelligent algorithm is proposed to optimize the multijoin query problem for smart grid data. The algorithm based on the Genetic Algorithm (GA), involves Guo Tao (GT) algorithm in crossover to maintain population diversity, and prevents premature convergence of the GA, and the mutation operator involves the Particle Swarm Optimization (PSO) to improve convergence speed and solution accuracy. The suggested algorithm ensures rapid processing of data and simulation result shows the query cost of the designed algorithm is lower than the GA, and it meets the smart grid data query requirement.