Research And Application On Multi-Join Query Optimization Of Database Based On Genetic Algorithm And Simulated Annealing

Nowadays, the information technology is developing very fast. Database has been a basic and necessary instrument for managing or excavating greatness potential of information. With the time passing, the amount of data goes increasingly, to query the data which adapt to users requirement is time-consuming. As a result, numbers of scholars developed the research on query optimization techniques in order to maintain the performance. The problem of multi-join query is very complicated, it largely influences the efficiency of data query, so optimization of multi-join query is one of the key problems.The algorithm of multi-join query optimization based on genetic algorithm with overall search ability and simulated annealing with local search ability are proposed by combining with characteristic of multi-join optimization. Starting an optimal-solution-search to the overall situation in a group of initial population, which is random selected. A new generation of population will be produced after the selection strategy, crossover and mutation. And then the simulated annealing is applied to those new populations, and the result is used as the unit of the next generation population. The above process is operated repeatedly and iterative, until the result meets the final qualification. The simulation experiment has proved its efficiency.