The Weapon Target Assignment in Adversarial Environments

In context of the current weapon target assignment (WTA) problem, consideration of target capability is often disregarded. To address this issue, we propose an optimized model for weapon target assignment in complex environments. Our model provides a comprehensive description and conducts a detailed analysis of constraints and objective functions in the context of adversarial weapon target assignment (AWTA). To efficiently tackle the assignment problem, we employ the multi-particle swarm optimization algorithm (MOPSO). During the optimization process, we incorporate learning factors and inertia weights to update particle positions, seamlessly implementing the model with a set of adjustable parameters. Experimental results demonstrate that our model effectively captures the concept of adversarial scenarios. Furthermore, by utilizing the MOPSO optimization algorithm, we achieve a superior Pareto front even for small-scale adversarial scenarios, successfully resolving the adversarial WTA problem. Our research contributes an effective optimization approach to address the weapon task assignment problem and offers valuable inspiration for future investigations into multi-objective research. In conclusion, our study reveals the potential of multi-objective optimization research in the context of weapon target assignment.