Abstract:

The impact of various random disturbances in the actual command and control environment of unmanned systems on problem modeling and solving of weapon target assignment was considered, and three types of uncertainty disturbance constraints were investigated. A multi-objective dynamic sensor weapontarget assignment model was established. By considering the issues of model property changes caused by disturbances and insufficient robustness of the traditional single-operator solving algorithm, a multi-operator constrained multi-objective evolutionary framework based on the deep Q-network was proposed. The algorithm described the convergence, diversity, and feasibility of the population in both the objective and decision spaces. It established a mapping model from states to reproduction operators, achieving a dynamically adjusted reproduction strategy. Simulation experiments validated the algorithm's excellent performance and robustness when solving models.

Key words: dynamic weapon target assignment, robustness, deep reinforcement learning, multi-operator reproduction, meta-heuristic algorithm