Weapon Target Assessment With Dynamic Hit Probabilities and Heterogeneous Targets: A Two-Stage Model and Solving Algorithms

As a classic optimization problem, the weapon target assignment (WTA) problem is widely studied. However, existing WTA models are mostly established on single-hit destruction and static hit probabilities and take the destroyed target value expectation as the optimization objective, which limits their application to more complex scenarios. In response to the above shortcomings, this paper establishes a two-stage WTA model for a more complex scenario. In this model, a complex network-based assessment method is introduced and applied to better quantify the damage degree of targets, a method for calculating the probability of a target being destroyed when struck by multiple weapons is proposed, and the stage division significantly reduced the dimension of the optimization model. A Monte Carlo tree search enhanced by a metaheuristic algorithm and a memetic algorithm with a coevolution mechanism are used to solve two sub-models of the two-stage WTA model, respectively. The simulation results demonstrate that the two targeted heuristic algorithms can produce better solutions than similar heuristic algorithms, and the WTA model proposed in this paper is applicable to complex scenarios involving dynamic hit probabilities and heterogeneous targets.