This paper presents a two-level protein folding optimization on a three-dimensional AB off-lattice model. The first level is responsible for forming conformations with a good hydrophobic core or a set of compact hydrophobic amino acid positions. These conformations are forwarded to the second level, where an accurate search is performed with the aim of locating conformations with the best energy value. The optimization process switches between these two levels until the stopping condition is satisfied. An auxiliary fitness function was designed for the first level, while the original fitness function is used in the second level. The auxiliary fitness function includes expression about the quality of the hydrophobic core. This expression is crucial for leading the search process to the promising solutions that have a good hydrophobic core and, consequently, improves the efficiency of the whole optimization process. Our differential evolution algorithm was used for demonstrating the efficiency of the two-level optimization. It was analyzed on well-known amino acid sequences that are used frequently in the literature. The obtained experimental results show that the employed two-level optimization improves the efficiency of our algorithm significantly, and that the proposed algorithm is superior to other state-of-the-art algorithms.
A Biogeography-Based Optimization (BBO) is also applied to PFO @cite_1 . This algorithm is based on the definition of habitats. Each habitat has its amount of species, and different habitats usually have different amounts of species. Within the algorithm, Habitat Suitability Index (HSI) is used to measure the quality of the habitat. Habitats with high HSI are suitable for survival. Thus, these habitats have low immigration rates and high emigration rates. On the contrary, habitats with low HSI have high immigration rates and low emigration rates. Additionally, BBO includes a mutation operator to avoid premature convergence, and elitism to avoid the degeneration phenomena. The improved BBO contains an improved migration process. In the migration process, a feature from a habitat is replaced by another feature from a different habitat. In the improved version, different features were selected from different habitats according to their emigration rates and their values with weights determine the features of the habitat. This algorithm was compared with the standard BBO and DE. The results show that the improved BBO outperforms all competitors.
@cite_1
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In recent years, many bio-inspired computation algorithms have been proposed to solve constraint problems. Biogeography-Based Optimization (BBO) is one of these newly proposed optimization algorithms. As a new way to solve complicated optimization problems, BBO has a quick convergence. In this paper, we proposed an improved BBO for solving protein structure prediction problems. Comparative experiments with standard BBO and differential evolution algorithm (DE) are also conducted, and the results demonstrate this improved BBO approach performs better in solving these complicated protein prediction problems.