Weapon-target assignment (WTA) which is crucial in cooperative air combat explores assigning weapons to targets with the objective of minimizing the threats from those targets. Based on threat functions, there are four WTA models constrained by the payload and other tactical requirements established. The improvements of ant colony optimization are integrated with respect to the rules of path selection, pheromone update, and pheromone concentration interval, and algorithm AScomp is proposed based on the elite strategy of ant colony optimization (ASrank). We add garbage ants to ASrank; when the pheromone is updated, the elite ants are rewarded and the garbage ants are punished. A WTA algorithm is designed based on the improved ant colony optimization (WIACO). For the purpose of demonstration of WIACO in air combat, a real-time WTA simulation algorithm (RWSA) is proposed to provide the results of average damage, damage rate, and kill ratio. The following conclusions are drawn: (1) the third WTA model, considering the threats of both sides and hit probabilities, is the most effective among the four; (2) compared to the traditional ant colony algorithm, the WIACO requires fewer iterations and avoids local optima more effectively; and (3) WTA is better conducted when any fighter is shot down or any fighter’s missiles run out than along with the flight.

We propose a hybrid discrete grey wolf optimizer (HDGWO) in this paper to solve the weapon target assignment (WTA) problem, a kind of nonlinear integer programming problems. To make the original grey wolf optimizer (GWO), which was only developed for problems with a continuous solution space, available in the context, we first modify it by adopting a decimal integer encoding method to represent solutions (wolves) and presenting a modular position update method to update solutions in the discrete solution space. By this means, we acquire a discrete grey wolf optimizer (DGWO) and then through combining it with a local search algorithm (LSA), we obtain the HDGWO. Moreover, we also introduce specific domain knowledge into both the encoding method and the local search algorithm to compress the feasible solution space. Finally, we examine the feasibility of the HDGWO and the scalability of the HDGWO, respectively, by adopting it to solve a benchmark case and ten large-scale WTA problems. All of the running results are compared with those of a discrete particle swarm optimization (DPSO), a genetic algorithm with greedy eugenics (GAWGE), and an adaptive immune genetic algorithm (AIGA). The detailed analysis proves the feasibility of the HDGWO in solving the benchmark case and demonstrates its scalability in solving large-scale WTA problems.

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Uncertainty should be taken into account when establishing multiobjective task assignment models for multiple unmanned combat aerial vehicles (UCAVs) due to errors in the target information acquired by sensors, implicit preferences of the commander for operational objectives, and partially known weights of sensors. In this paper, we extend the stochastic multicriteria acceptability analysis-2 (SMAA-2) method and combine it with integer linear programming to achieve multiobjective task assignment for multi-UCAV under multiple uncertainties. We first represent the uncertain target information as normal distribution interval numbers so that the values of criteria (operational objectives) concerned can be computed based on the weighted arithmetic averaging operator. Thus, we obtain multiple criteria value matrices for each UCAV. Then, we propose a novel aggregation method to generate the final criteria value matrix based on which the holistic acceptability indices are computed by the extended SMAA-2 method. On this basis, we convert the task assignment model with uncertain parameters into an integer linear programming model without uncertainty so as to implement task assignment using the integer linear programming method. Finally, we conduct a case study and demonstrate the feasibility of the proposed method in solving the multiobjective task assignment problem multi-UCAV under multiple uncertainties.