A new approach to solve the mission assignment problem for cooperative UCAVs using immune particle swarm optimizations

This paper first builds a mathematical model for the mission assignment problem (MAP) of cooperative multiple uninhabited combat aerial vehicles (UCAVs). To address challenges posed by the specific MAP problem, we propose a new immune particle swarm optimization (NIPSO) approach through incorporating immunity memory, diversity clone, and immune selection in artificial immune algorithm into standard PSO. The simulation results achieved on a typical scenario show that our NIPSO approach for multiple UCAVs is capable of substantially speeding up convergence and has stronger ability to find the global optimum than that of classical PSO. The MAP solution for cooperative UCAVs is significantly improved.