  This paper presents a two-phase protein folding optimization on a
three-dimensional AB off-lattice model. The first phase 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 phase, where an accurate search is performed with the aim of locating
conformations with the best energy value. The optimization process switches
between these two phases until the stopping condition is satisfied. An
auxiliary fitness function was designed for the first phase, while the original
fitness function is used in the second phase. The auxiliary fitness function
includes an 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 two-phase 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-phase optimization
improves the efficiency of our algorithm significantly and that the proposed
algorithm is superior to other state-of-the-art algorithms.
