Multiple sequence alignments are the core of bioinformatics and exploring life science. The emerging methods of information technology explode the area of RNA sequencing. The approach of sequencing moves to next-generation sequencing (NGS). Recently various researchers proposed genome sequencing methods which are based on CUDA, parallel programming, and swarm intelligence-based algorithms. The incremental approach of algorithms for genome sequencing increases score points and reduces the execution time of analysis. This paper proposed hybrid methods for accelerating multiple sequence alignment. The proposed algorithm combines particle swarm optimization and a genetic algorithm. This algorithm is very efficient in terms of score points of alignment and reduces the execution time of patterns. The proposed algorithm was tested on MATLAB tools and validated with three datasets: chimpanzee, mouse, and dog. The experimental results suggest that the proposed algorithm is very efficient than existing algorithms based on CUDA and GA.

MSA, CUDA, GA, PSO, CPU, bioinformatics, swarm intelligence