Parallel ant colony optimization for the HP protein folding problem

Abstract

Ant ColonyOptimisation (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× 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.