论文标题
大规模传感器选择问题的随机组绿色方法
Randomized Group-Greedy Method for Large-Scale Sensor Selection Problems
论文作者
论文摘要
提出了随机组蛋白质方法及其用于大规模传感器选择问题的自定义方法。随机贪婪传感器选择算法直接应用于组蛋白质方法，还考虑了一种自定义方法。在自定义方法中，使用常见的贪婪方法或其他低成本方法选择了压缩传感器候选的一部分。由于压缩传感器候选物，该策略弥补了解决方案的恶化。根据实验的D和E-最佳设计实施了所提出的方法，并使用随机生成的传感器候选矩阵进行数值实验，其潜在传感器位置为10,000-1,000,000。与原始的群怪怪事方法相比，该方法比原始组盖怪方法获得的方法可以提供更好的优化结果。这是因为通过随机算法候选压缩传感器，可以增加组蛋白质方法的组大小。在实际数据集中也获得了类似的结果。所提出的方法对于电子极端标准有效，在这种标准中，由于没有目标函数的非义能，因此很难通过共同贪婪方法优化的目标函数。当前方法的想法可以使用贪婪算法提高所有优化的性能。
The randomized group-greedy method and its customized method for large-scale sensor selection problems are proposed. The randomized greedy sensor selection algorithm is applied straightforwardly to the group-greedy method, and a customized method is also considered. In the customized method, a part of the compressed sensor candidates is selected using the common greedy method or other low-cost methods. This strategy compensates for the deterioration of the solution due to compressed sensor candidates. The proposed methods are implemented based on the D- and E-optimal design of experiments, and numerical experiments are conducted using randomly generated sensor candidate matrices with potential sensor locations of 10,000--1,000,000. The proposed method can provide better optimization results than those obtained by the original group-greedy method when a similar computational cost is spent as for the original group-greedy method. This is because the group size for the group-greedy method can be increased as a result of the compressed sensor candidates by the randomized algorithm. Similar results were also obtained in the real dataset. The proposed method is effective for the E-optimality criterion, in which the objective function that the optimization by the common greedy method is difficult due to the absence of submodularity of the objective function. The idea of the present method can improve the performance of all optimizations using a greedy algorithm.
