The solution of target assignment problem in command and control decision-making behaviour simulation

A multi-objective sparse evolutionary framework for large-scale weapon target assignment based on a reward strategy

 The asset-based weapon target assignment (ABWTA) problem is one of the important branches of the weapon target assignment (WTA) problem. Due to the current large-scale battlefield environment, the ABWTA problem is a multi-objective optimization problem (MOP) with strong constraints, large-scale and sparse properties. The novel model of the ABWTA problem with the operation error parameter is established. An evolutionary algorithm for large-scale sparse problems (SparseEA) is introduced as the main framework for solving large-scale sparse ABWTA problem. The proposed framework (SparseEA-ABWTA) mainly addresses the issue that problem-specific initialization method and genetic operators with a reward strategy can generate solutions efficiently considering the sparsity of variables and an improved non-dominated solution selection method is presented to handle the constraints. Under the premise of constructing large-scale cases by the specific case generator, two numerical experiments on four outstanding multi-objective evolutionary algorithms (MOEAs) show Runtime of SparseEA-ABWTA is faster nearly 50% than others under the same convergence and the gap between MOEAs improved by the mechanism of SparseEA-ABWTA and SparseEA-ABWTA is reduced to nearly 20% in the convergence and distribution.

Study on Master Slave Interaction Model Based on Stackelberg Game in Distributed Environment

In view of the problems such as low efficiency, difficulty in resolving local conflicts and lack of practical application scenarios, existing in the interaction model of multi-agent systems in a distributed environment, a multi-master multi-slave interaction model was designed based on the Stackelberg game, which is applied to the interaction game problem between the controller and the participant in the command and control process. Through optimizing the Stackelberg game model and multi-attribute decision-making, the multi-master, multi-slave, multi-agent system of the Stackelberg game was designed, and the closed loop problem under the Stackelberg game is solved for dimension reduction and optimal function value. Finally, through the numerical derivation simulation and the training results of related system data, the high efficiency and strong robustness of the model were verified from multiple perspectives, and this model algorithm was proved to be true and highly efficient.