Solving multiple sequence alignment problems by using a swarm intelligent optimization based approach

<span lang="EN-US">In this article, the alignment of multiple sequences is examined through swarm intelligence based an improved particle optimization (PSO). A random heuristic technique for solving discrete problems and realistic estimation was recently discovered in PSO. The PSO approach a nature-inspired on movement. Thus, each solution encoded as “chromosomes” genetic algorithm (GA). Based objective function, fitness function designed to maximize suitable components sequence reduce unsuitable sequence. availability public benchmark data set such Bali base seen assessment proposed system performance, with potential reveal adapting better performance. This compared few existing approaches deoxyribonucleic acid (DNA) or ribonucleic (RNA) (DIALIGN), PILEUP8, hidden Markov model training (HMMT), rubber band technique-genetic (RBT-GA) ML-PIMA. In many cases, experimental results are well implemented other approaches.</span>