  This paper presents a novel Differential Evolution algorithm for protein
folding optimization that is applied to a three-dimensional AB off-lattice
model. The proposed algorithm includes two new mechanisms. A local search is
used to improve convergence speed and to reduce the runtime complexity of the
energy calculation. For this purpose, a local movement is introduced within the
local search. The designed evolutionary algorithm has fast convergence speed
and, therefore, when it is trapped into the local optimum or a relatively good
solution is located, it is hard to locate a better similar solution. The
similar solution is different from the good solution in only a few components.
A component reinitialization method is designed to mitigate this problem. Both
the new mechanisms and the proposed algorithm were analyzed on well-known amino
acid sequences that are used frequently in the literature. Experimental results
show that the employed new mechanisms improve the efficiency of our algorithm
and that the proposed algorithm is superior to other state-of-the-art
algorithms. It obtained a hit ratio of 100% for sequences up to 18 monomers,
within a budget of $10^{11}$ solution evaluations. New best-known solutions
were obtained for most of the sequences. The existence of the symmetric
best-known solutions is also demonstrated in the paper.
