APPLICATION OF ANT COLONY OPTIMIZATION ALGORITHM FOR 2D HYDROPHOBIC-POLAR PROTEIN FOLDING MODEL
蚂蚁群落优化算法在蛋白质折叠二维亲-疏水格点模型中的应用

Protein folding problem is one of the most prominent problems in bioinformatics, and hydrophobic-polar model (HP model) is a wide abstractional model in study of this problem. A new algorithm of ant colony optimization (ACO) was proposed for the HP model's optimal problem, which was a non-deterministic polynomial problem (NP-hard problem). Two novel operations, clone and elimination, were added into the normal ACO algorithm, which improved the algorithm's computational efficiency greatly. Execution for standard benchmark instances indicated that the efficiency of this new algorithm is better than that of the existent algorithms, such as Monte Carlo algorithm and genetic algorithm.