Multiple Sequence Alignment using a Multiobjective Artificial Bee Colony Algorithm

Abstract

Multiple sequences alignment is an essential task in many bioinformatics analyses. However, it is a challenging problem due to its exponential complexity. Although many approaches have been proposed for solving the MSA problem, finding the best alignment of the MSA remains an open problem. In this work, we propose a new multiobjective optimization approach based on the Artificial Bee Colony (ABC) algorithm to solve the MSA problem. The ABC algorithm is inspired by the intelligent behavior of honey bee swarms. Our proposed approach optimizes two objective functions; the sum-of-pairs (SP) function and entropy. Optimizing these objective functions represents a trade off between maximizing the SP function and minimizing the entropy to choose the most suitable alignment. Experiments are conducted on 12 datasets from BAliBASE 3.0. The experimental results show that our proposed approach achieves better alignments than Clustal in four of the tested datasets. In general, the proposed approach performs better on RV12 datasets.