The exponential increase in the rate of data size is much higher than the increase in the speed of the computer, which has given much focus to search algorithms in the research literature. Finding an item in an ordered dataset is an efficient method in the data processing. However, binary and interpolation algorithms are commonly used to search ordered datasets in many applications. In this paper, we propose a hybrid algorithm for searching ordered datasets based on the idea of interpolation and binary search. The proposed algorithm is called Interpolated Binary Search (IBS). It is well known that the performance of traditional interpolation search depends specifically on key distribution, and its performance degrades significantly in non-uniform distributed datasets. Therefore, our proposed algorithm works efficiently on various distribution datasets. In particular, IBS aims to search datasets of unknown distribution or datasets that change dynamically and produce a dynamic distribution. Experimental results show that IBS performs better compared to other algorithms that use a similar approach.

数据大小速率的指数增长远高于计算机速度的增长，这在研究文献中非常关注搜索算法。在有序数据集中查找项目是数据处理中的一种有效方法。但是，二进制和插值算法通常用于在许多应用程序中搜索有序数据集。在本文中，我们提出了一种基于插值和二分搜索思想的搜索有序数据集的混合算法。所提出的算法称为内插二分搜索（IBS）。众所周知，传统插值搜索的性能具体取决于密钥分布，在非均匀分布的数据集中其性能显着下降。所以，我们提出的算法在各种分布数据集上都有效。特别是，IBS 旨在搜索未知分布的数据集或动态变化并产生动态分布的数据集。实验结果表明，与使用类似方法的其他算法相比，IBS 的性能更好。