数据立方体实体化的多目标遗传算法

摘要

The data cube selection problem is known to be an NP-hard problem. This paper presents an evolutionary algorithm in which query cost and maintenance cost are considered separately for constrained optimization and more effectively addresses the complex view-selection problem. The experimental results show that the multi-objective optimization algorithm has better performance, especially in the distribution of the obtained Pareto front.%多维数据实视图选择问题是一个NP完全问题.提出一种基于约束的多目标优化遗传算法,将查询代价和维护代价分开考虑,更有效地解决复杂的实视图选择问题.实验结果表明,该算法具有更好的性能,特别是在获得的Pareto前沿的分布性上.