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Parallel Ant Colony Optimization for the HP Protein Folding Problem

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Bioinformatics and Biomedical Engineering (IWBBIO 2016)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9656))

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Abstract

Ant Colony Optimisation (ACO) is a bio-inspired population-based metaheuristic which emulates the ant colony’s behavior to solve problems computationally. Indeed, it is a swarm-based algorithm as it needs the interactions among all ants to provide good solutions to a particular problem. This collective computation is theoretically well-suited for parallelisation as several ants run in parallel looking for solutions, sharing their findings among them. In this paper, we design an ACO metaheuristic to solve the Protein Folding Problem using a simplified model (HP) that identifies amino acids like Hydrophobic (H) or Polar (P), attending to the attraction or the rejection that the amino acid present against water. We also propose a parallel ACO version applied to the HP model on Graphics Processing Units (GPUs) using Compute Unified Device Architecture (CUDA). Our results reveal up to 7\(\times \) speed-up factor compared to a sequential counterpart version. Results and conclusions about this parallel version suggests a broader area of inquiry, where researchers within the fields of Bioinformatics may learn to adapt similar problems to the tupla of an optimization method and GPU architecture.

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Acknowledgements

This work has been funded by grants from the Fundación Séneca of the Región of Murcia (18946/JLI/13) and by the Nils Coordinated Mobility under grant 012-ABEL-CM-2014A, in part financed by the European Regional Development Fund (ERDF). We also thank Nvidia for the hardware donation under GPU Research and Educational Center Program.

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Authors and Affiliations

  1. Bioinformatics and High Performance Computing Research Group (BIO-HPC), Computer Science Department, Universidad Católica San Antonio de Murcia (UCAM), Murcia, Spain

    Antonio Llanes, Carlos Vélez, Antonia M. Sánchez, Horacio Pérez-Sánchez & José M. Cecilia

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  1. Antonio Llanes
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  2. Carlos Vélez
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  3. Antonia M. Sánchez
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  4. Horacio Pérez-Sánchez
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  5. José M. Cecilia
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Corresponding author

Correspondence to José M. Cecilia .

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Editors and Affiliations

  1. Universidad de Granada, Granada, Spain

    Francisco Ortuño

  2. Universidad de Granada, Granada, Spain

    Ignacio Rojas

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Llanes, A., Vélez, C., Sánchez, A.M., Pérez-Sánchez, H., Cecilia, J.M. (2016). Parallel Ant Colony Optimization for the HP Protein Folding Problem. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2016. Lecture Notes in Computer Science(), vol 9656. Springer, Cham. https://doi.org/10.1007/978-3-319-31744-1_54

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  • DOI: https://doi.org/10.1007/978-3-319-31744-1_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31743-4

  • Online ISBN: 978-3-319-31744-1

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