Query Optimization of Distributed Database Based on Parallel Genetic Algorithm and Max-Min Ant System

Since the era of big data is coming, the first important problem is how to enhance the speed of database query. For the query optimization of distributed database, the speed of query depends on the data transfer and order of join. The cost model minimizing communication cost is the emphasis of research. Parallel Genetic Algorithm-Max-Min Ant System was proposed to seek a best query execution plan, which combines faster convergence of Genetic Algorithm, globally search ability of Max-Min Ant System and parallel property of both them. The experiment results show that the proposed algorithm is effective for query processing of multi-join, and plays important role in improving the performance of distributed database.