Abstract
This paper reports the use of the Artificial Bee Colony algorithm (ABC) for protein structure prediction using the three-dimensional hydrophobic-polar model with side-chains (3DHP-SC). Two parallel approaches for the ABC were implemented: a master-slave and a hybrid-hierarchical. Experiments were done for tuning the parameters of the ABC, as well as to adjust the load balance in a cluster-based computing environment. The performance of the parallel models was compared with a sequential version for 4 benchmark instances. Results showed that the parallel models achieved a good level of efficiency and, thanks to the co-evolution effect, the hybrid-hierarchical approach improves the quality of solutions found.
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Vargas Benítez, C.M., Lopes, H.S. (2010). Parallel Artificial Bee Colony Algorithm Approaches for Protein Structure Prediction Using the 3DHP-SC Model. In: Essaaidi, M., Malgeri, M., Badica, C. (eds) Intelligent Distributed Computing IV. Studies in Computational Intelligence, vol 315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15211-5_27
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DOI: https://doi.org/10.1007/978-3-642-15211-5_27
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