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 phylo...

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Veröffentlicht in:2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications S. 713 - 720
Hauptverfasser: Santander-Jimenez, Sergio, Vega-Rodriguez, M. A., Gomez-Pulido, J. A., S'nchez-Perez, J. M.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.07.2012
Schlagworte:
Algorithm design and analysis
Artificial Bee Colony
Multicore Architectures
Multicore processing
Multiobjective Optimization
Optimization
Parallel processing
Phylogenetic Inference
Phylogeny
Topology
Vegetation
ISBN:1467316318, 9781467316316
ISSN:2158-9178
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Zusammenfassung: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.
ISBN:1467316318
9781467316316
ISSN:2158-9178
DOI:10.1109/ISPA.2012.105