Notice of RetractionDifferential Evolution based on improved mutation strategy

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

Differential Evolution (DE) is well-known as a simple and efficient scheme for global optimization over continuous spaces. However, the mutation strategies used in DE greatly affect its performance. In this paper, we present a novel DE variant with an improved mutation strategy. The proposed approach, called IDE, is motivated by the idea of Particle Swarm Optimization (PSO), in which each individual flies to better positions by learning its previous best and other best individuals´ experiences. In order to verify the performance of IDE, we test it on ten famous benchmark functions. The simulation results show that IDE outperforms classical DE, self-adaptive DE (SaDE), and DE with neighborhood search (NSDE) on the majority of test problems.