A Novel Population-based Optimization for Multiple Sequence Alignment in Protein Sequencing

Anjali Goswami1

Kamlesh Kumar Dubey2,Email

1Department of Mathematics and Statistics, College of Science and Theoretical Studies, Saudi Electronic University, Riyadh-13323.
2Department of Applied Science and Humanities, Invertis University, Bareilly, India-243123.
#These authors contributed to this work equally.

 

Abstract

This paper proposes a novel Population-based optimization (PBO) approach to use in conjunction with an upgraded migration operator. It can tackle multiple sequence alignment (MSA) problems which can be implemented to solve the protein sequences problem. In the present state of development, it is a unique population-based algorithm based on genetic algorithms. Further, the lack of population variety and the sluggish convergence pace can be ascribed to the lack of population variety. This improved migration operator absorbs more information from various habitat types, maintains population variation, and maintains its ability to exploit new possibilities. The performance of the proposed approach has been compared to that of many current techniques, including VDGA, MOMSA, and GAPAM, using publicly available benchmark datasets, with the results showing that it outperforms them all (such as the Bali base). It has been observed that in most cases, the suggested approach outperforms and/or is competitive with the existing techniques.

A Novel Population-based Optimization for Multiple Sequence Alignment in Protein Sequencing