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Parallel Ant Colony Optimization for 3D Protein Structure Prediction using the HP Lattice Model

  • Daniel Chu
  • Albert Zomaya
Part of the Studies in Computational Intelligence book series (SCI, volume 22)

Abstract

Protein structure prediction (also known as the protein folding problem) studies the way in which a protein will ‘fold’ into its natural state. Due to the enormous complexities involed in accuratly predicting protein structures, many simplifications have been proposed. The Hydrophobic-Hydrophilic (HP) method is one such method of simplifying the problem. In this chapter we introduce a novel method of solving the HP protein folding problem in both two and three dimensions using Ant Colony Optimizations and a distributed programming paradigm. Tests across a small number of processors indicate that the multiple colony distributed ACO (MACO) approach outperforms single colony implementations. Experimental results also demonstrate that the proposed algorithms perform well in terms of network scalability.

Keywords

Candidate Solution Protein Structure Prediction High Quality Solution Local Search Phase Pheromone Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer 2006

Authors and Affiliations

  • Daniel Chu
    • 1
  • Albert Zomaya
    • 1
  1. 1.School of IT, Faculty of ScienceUniversity of SydneyAustralia

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