基于蚁群遗传混合算法改进的连接查询研究

连接查询优化技术对提升数据库性能至关重要，提出一种改进的连接查询算法，结合Wander Join连接查询算法，使用蚁群遗传混合算法对连接顺序进行优化。执行新的连接计划后，用剪枝策略降低样本的连接复杂度，达到了减少存储代价的目的。 理论分析和基于TPC-H数据集、TPC-DS数据集的算法对比实验表明，在多表连接的样本置信区间大于或等于95%的条件下，根据选择率的不同，加入蚁群遗传混合算法和剪枝策略的连接查询算法的相对错误率与Wander Join连接查询算法相比下降了20%～70%。

Connection query based on improved ant colony genetic algorithm

Connection query optimization technique is very important to improve database performance. This paper proposes an improved connection query algorithm, which combines the Wander Join query algorithm and the ant colony genetic hybrid algorithm to optimize the connection order. After executing the new connection plan, pruning strategy is used to decrease the complexity of sample connection, thus achieving the purpose of reducing storage cost. Theoretical analysis and comparative experiment on TPC-H data set and TPC-DS data set are carried out. Experimental results prove that, under the condition that the sample confidence interval of multi-table connection is greater than or equal to 95%, the connection query algorithm combing the ant colony genetic hybrid algorithm and pruning strategy can reduce the relative error rate by 20% to 70% in comparison to the Wander Join query algorithm.