Abstract
To predict protein structure based on Hydrophobic-Polar model (HP model) in two-dimensional space is called 2D HP protein folding problem. Ant Colony Optimization (ACO), which is inspired by the foraging behavior of ants, is a popular heuristic approach for solving combinatorial optimization problems. This paper presents a method of solving the 2D HP protein folding problem by parallel ACO algorithm. Each ant colony is able to search the best solution guided by the shared pheromone matrix which accumulates the good experience achieved by previous populations. The shared pheromone matrix can integrate all the search knowledge found by parallel colonies. Experimental results show that the parallel implementation performs better comparing with the other ACO solutions.
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