Notice of RetractionEntropy Regularization method for Coordinated Multiple Target Assignment

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.

Air Combat Decision-Making for Coordinated Multiple Target Assignment is an important yet difficult problem in the modern information warfare. Previous methods, such as neural network, genetic algorithm, ant colony algorithm, particle swarm optimization and auction algorithm, used to resolve this problem have proved to be either too brittle or not stable. To address this problem, a new continuous and distributed method based on Entropy Regularization is proposed. Based on the Probability Collective, the discrete problem is converted to be continuous by adding an Entropy Regular Item in the original basis of the global utility function, in order to amend the pathosis of the original function. Then, the optimization probability algorithm updating is done by jointly minimizing a Lagrangian of their joint state probability distribution. The experiment results show that as compared with conventional methods, the proposed method is be able to converge to the global optimum with more precision and stability.