###### Abstract  
Dynamic weapon target assignment (DWTA) is an effective method to solve the multi-stage battlefield fire optimization problem, which can reflect the actual combat scenario better than static weapon target assignment (SWTA). In this paper, a meaningful and effective DWTA model is established, which contains two practical and conflicting objectives, namely, maximizing combat benefits and minimizing weapon costs. Moreover, the model contains limited resource constraints, feasibility constraints and fire transfer constraints. The existence of multi-objective and multi-constraint makes DWTA more complicated. To solve this problem, an improved multiobjective particle swarm optimization algorithm (IMOPSO) is proposed in this paper. Various learning strategies are adopted for the dominated and non-dominated solutions of the algorithm, so that the algorithm can learn and evolve in a targeted manner. In order to solve the problem that the algorithm is easy to fall into local optimum, this paper proposes a search strategy based on simulated binary crossover (SBX) and polynomial mutation (PM), which enables elitist information to be shared among external archive and enhances the exploratory capabilities of IMOPSO. In addition, a dynamic archive maintenance strategy is applied to improve the diversity of non-dominated solutions. Finally, this algorithm is compared with three state-of-the-art multiobjective optimization algorithms, including solving benchmark functions and DWTA model in this article. Experimental results show that IMOPSO has better convergence and distribution than the other three multiobjective optimization algorithms. IMOPSO has obvious advantages in solving multiobjective DWTA problems. dynamic weapon target assignment (DWTA); multiobjective particle swarm optimization algorithm (MOPSO); multiobjective optimization Article  
Solving the Dynamic Weapon Target Assignment Problem  
1  
[1] 2021  
the weapon combat zone. As the battlefield situation is constantly changing, each weapon-target-stage pair will be assigned a corresponding time window. DWTA requires that the change of battlefield situation information should be considered when assigning tasks at different stages. The DWTA algorithm must give the result of allocation scheme in a limited time, which can provide the basis for subsequent decision-making. In recent years, there have been many methods to solve WTA problems, including traditional methods and intelligent algorithms. Traditional methods include goal programming [10], game theoretic framework [11], Lagrangian relaxation method [12], etc. These algorithms can solve WTA problems with smaller dimensions, but their global optimization ability is poor, which are unable to meet the actual needs. Intelligent algorithm is widely used to solve WTA problems because of its high optimization precision and fast convergence speed. [PERSON] et al. [13] put forward a new selection algorithm, which used adaptive chaos optimization and clonal selection method to solve WTA. [PERSON] et al. [14] used a parallel simulated annealing algorithm to solve the WTA problem. [PERSON] et al. [15] proposed an evolutionary decision algorithm including genetic algorithm and memetic algorithm to solve the DWTA problem. [PERSON] et al. [16] introduced the population initialization method for specific problems into evolutionary algorithm, improved the selection operation mechanism, and improved the efficiency of solving the SWTA problem. [PERSON] et al. [17] proposed an ant colony optimization algorithm based on elite strategy, which improved the strategies of path selection and pheromone update and solved four WTA models. Although these methods have a certain improvement in optimization accuracy compared with traditional methods, it is necessary to further improve the optimization ability and solution quality of these algorithms to meet the operational requirements of the battlefield. WTA is a typical combinatorial constraint optimization problem.