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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.