The alignment among three or more nucleotides/amino acids sequences at the same time is known as multiple sequence alignment (MSA), a nondeterministic polynomial time (NP)-hard optimization problem. The time complexity of finding an optimal alignment raises exponentially when the number of sequences to align increases. In this work, we deal with a multiobjective version of the MSA problem wherein the goal is to simultaneously optimize the accuracy and conservation of the alignment. A parallel version of the hybrid multiobjective memetic metaheuristics for MSA is proposed. To evaluate the parallel performance of our proposal, we have selected a pull of data sets with different number of sequences (up to 1000 sequences) and study its parallel performance against other well-known parallel metaheuristics published in the literature, such as MSAProbs, tree-based consistency objective function for alignment evaluation (T-Coffee), Clustal [Formula: see text], and multiple alignment using fast Fourier transform (MAFFT). The comparative study reveals that our parallel aligner obtains better results than MSAProbs, T-Coffee, Clustal [Formula: see text], and MAFFT. In addition, the parallel version is around 25 times faster than the sequential version with 32 cores, obtaining an efficiency around 80%.
Keywords: memetic computing; metaheuristic; multiobjective optimization; multiple sequence alignment..