Largest Shift String Matching Algorithm: Blend of Berry Ravindran, Zhu-Takaoka and Back & Forth Matching Algorithm

With the exponential growth of biological databases, it is a highly challenging and essential task to find the exact string from these databases. Various string pattern searching strategies played an essential role in solving string search problems. Mainly, the string pattern matching algorithm aims to reduce the running time which depends on attempts and character comparisons. Our paper proposed a new hybrid strategy, the Largest Shift Algorithm (LSA), to solve these string pattern matching problems more effectively. Our suggested new hybrid algorithm combines the most advantageous characteristics of Berry Ravindran, Zhu-Takaoka, and a customized Back & Forth Matching (BFM) algorithm. These three algorithms were chosen as they perform better in the tests for counting attempts and character comparisons. Three distinct types of algorithms were tested to analyze the performance of our proposed LSA algorithm, discussed in the literature which are BRR, maximum-shift, and quick-search algorithms. We have used English text, DNA, and protein sequences as data for their different nature. The proposed algorithm outperforms the previous algorithms in terms of performance such as runtime, total attempts, and comparisons of character.