A Hybrid Monte Carlo Ant Colony Optimization Approach for Protein Structure Prediction in the HP Model

Andrea G. Citrolo
(Università degli Studi di Milano-Bicocca)
Giancarlo Mauri
(Università degli Studi di Milano-Bicocca)

The hydrophobic-polar (HP) model has been widely studied in the field of protein structure prediction (PSP) both for theoretical purposes and as a benchmark for new optimization strategies. In this work we introduce a new heuristics based on Ant Colony Optimization (ACO) and Markov Chain Monte Carlo (MCMC) that we called Hybrid Monte Carlo Ant Colony Optimization (HMCACO). We describe this method and compare results obtained on well known HP instances in the 3 dimensional cubic lattice to those obtained with standard ACO and Simulated Annealing (SA). All methods were implemented using an unconstrained neighborhood and a modified objective function to prevent the creation of overlapping walks. Results show that our methods perform better than the other heuristics in all benchmark instances.

In Alex Graudenzi, Giulio Caravagna, Giancarlo Mauri and Marco Antoniotti: Proceedings Wivace 2013 - Italian Workshop on Artificial Life and Evolutionary Computation (Wivace 2013), Milan, Italy, July 1-2, 2013, Electronic Proceedings in Theoretical Computer Science 130, pp. 61–69.
Published: 30th September 2013.

ArXived at: http://dx.doi.org/10.4204/EPTCS.130.9 bibtex PDF
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