Multiple Sequence Alignment based on Enhanced Brainstorm Optimization Algorithm with dynamic population size(EBSODP)

Abstract

                                Multiple Sequence Alignment is a significantresearch problem in the feild of Bioinformatics.Variousmethodshave been developed for computing optimal sequence alignment, But deriving optimum accuracy is still a challengein  multiple alignments. One of the new meta heuristic approach is Brain storm Optimization which can efficiently solve more optimization applications. However  premature convergence occurs  due to the inability in  maintaining the diverging populations and  reaching  local optima in BSO.In order to  address this shortfall in  premature convergence, we proposed a  new adaptive dynamic population size  BSO in our paper. This enhanced mechanism  will dynamically increase or decrease the solution set in the search space for every iteration to maintain population diversity. We intend to use Enhanced Brain Storm Optimization Algorithm with dynamic population (EBSODP-MSA) to explore more optimality in alignments of multiple sequences. The experiments derived with the datasets shows that the proposed algorithm performs well in obtaining the nearest and optimal fitness score compared to the original BSO and other evolutionary approaches.