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

Protein Folding Optimization using Differential Evolution Extended with Local Search and Component Reinitialization