Dynamic target assignment method for UCAV fleet using memory enhanced dynamic UMDA

Aiming at the dynamic target assignment problem in anti-ground mission of an unmanned combat aerial vehicle (UCAV) fleet, a weapon target assignment (WTA) model for associated targets is built up. A memory enhanced dynamic univariate marginal distribution algorithm (MDUMDA) is proposed to solve the problem at hand. In this algorithm, the probability model is utilized to save and reuse the historic evolutionary information. Simulation results show that the WTA model is reasonable, the MDUMDA can effectively solve dynamic WTA, and its performance is significantly better than the random immigration algorithm.