Notice of RetractionAn escalating multi-objective DE algorithm

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.

In this paper, a multi-objective DE algorithm based on escalating strategy will be proposed. The main idea of this escalating strategy is to re-generate the whole evolutionary population with some technology, which results in a new population significantly indifferent from the old one while inheriting the evolutionary information from the history. By this way, the performance on global convergence can be enhanced, and premature can be avoided simultaneously. A neighborhood search procedure is imposed on some selected Pareto solutions to accelerate the evolution process for reaching a global Pareto set with well distribution. Some typical multi-objective optimization test problems are taken to solve with escalation DE and non-escalation DE respectively to verify the effectiveness of the new algorithm. The details about how to select appropriate escalating parameters and their effect on the performance of EMDE are also investigated to show that the EMDE with random flexible factor has some advantage over than that of fixed flexible factor.