Notice of RetractionA robust algorithm for multi-objective optimization problem

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 the past decades, the main focus of optimization studies including multi-objective optimization was placed on finding the global optimum or Pareto-optimal solutions. However, in practical engineering, users may not always be interested in finding the so-called global best solutions, particularly when these Pareto-optimal solutions are quite sensitive to the variable perturbations, which cannot be avoided in practice. In such cases, practitioners are interested in finding the robust solutions which are less sensitive to small perturbations or noise in variables. In this paper, a new robust multi-objective optimization algorithm is proposed, where the emphasis is to find the robust frontier instead of the global Pareto-optimal frontier. In the proposed algorithm, the robustness preference of user is incorporated into the optimization process to guide the optimization. A series of simulations are presented to validate the feasibility of the algorithm. The results show that the algorithm has a good robust and preference performance.