Evaluating the Performance of a Parallel Multiobjective Artificial Bee Colony Algorithm for Inferring Phylogenies on Multicore Architectures

A wide variety of optimization problems requires the combination of Bioinspired and Parallel Computing to address the complexity needed to get optimal solutions in reduced times. The multicore era allows the researcher to exploit modern arqitectures to resolve these NP-Hard problems. Inferring phylogenetic trees which describe a hypothesis of the evolution of species is a well-known example of this kind of problems. As the space of possible tree topologies increases exponentially with the number of species, exhaustive searches cannot be applied. Also, additional difficulties arise when we must consider simultaneously multiple optimality measures to resolve the problem. In this paper, we report a performance study on multicore machines of a parallel multiobjective adaptation of the Artificial Bee Colony algorithm for inferring phylogenies according to the maximum parsimony and maximum likelihood criteria. Experimental results reveal that our proposal can improve other approaches based on advanced High Performance Computing techniques on large data sets.