Reconnaissance Task Assignment Method for UAV Formation in Urban Warfare

To address the difficultly of obtaining an optimal solution with both strong global search capability and high timeliness in the reconnaissance task assignment of UAV formations in urban warfare, a hybrid algorithm combining an improved Artificial Immune Algorithm (AIA) and a Traveling Salesman Problem (TSP)-based Contract Net Protocol (CNP) is proposed. First, adaptive crossover and mutation operators are designed in the AIA to avoid falling into local optima. Meanwhile, a memory operator is introduced to record high-quality solutions and eliminate inferior ones, so as to accelerate the convergence of the algorithm. Finally, the CNP integrated with TSP rules enables simultaneous bidding for multiple dynamic tasks, which improves task allocation efficiency. Simulation results show that the proposed algorithm meets the expected requirements in terms of global optimality and timeliness.