Abstract
AbstractThe multiple sequence alignment (MSA) problem is essential in biological research for finding specific relationship between the biologic sequences and their functions. This paper proposes a multi-objective artificial bee colony optimization algorithm for MSA (MOABC-MSA), which uses three kinds of searching to optimize a multi-objective MSA problem. The employed bee searching aims to make the solutions converge to the Pareto front (PF) of the problem; the onlooker bee accelerates the convergence speed; the scout bee facilitates the algorithm to avoid the local optimal. A comparative experiment is implemented on BAliBASE 3.0, a MSA benchmark. Experimental results show that the proposed algorithm has competitive performance with state-of-the-art metaheuristic algorithms.KeywordsMultiple sequence alignmentMulti-objective optimizationArtificial bee colony optimization
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