Two-Stage Uncertain UAV Combat Mission Assignment Problem Based on Uncertainty Theory

Abstract

Based on uncertainty theory, this paper studies the problem of unmanned aerial vehicle (UAV) combat mission assignment under an uncertain environment. First, considering both the target value, which is the combat mission benefit gained from attacking the target, and the unit fuel consumption of UAV as uncertain variables, an uncertain UAV combat mission assignment model is established. And according to decisions under the realization of uncertain variables, the first stage generates an initial mission allocation scheme corresponding to the realization of target value, while the second stage dynamically adjusts the scheme according to the realization of unit fuel consumption; a two-stage uncertain UAV combat mission assignment (TUCMA) model is obtained. Then, because of the difficulty of obtaining analytical solutions due to uncertainty and the complexity of solving the second stage, the TUCMA model is transformed into an expected value-effective deterministic model of the two-stage uncertain UAV combat mission assignment (ETUCMA). A modified particle swarm optimization (PSO) algorithm is designed to solve the ETUCMA model to get the expected value-effective solution of the TUCMA model. Finally, experimental simulations of multiple UAV combat task allocation scenarios demonstrate that the proposed modified PSO algorithm yields an optimal decision with maximum combat mission benefits under a maximum iteration limit, which are significantly greater benefits than those for the mission assignment achieved by the original PSO algorithm. The proposed modified PSO exhibits superior performance compared with the ant colony optimization algorithm, enabling the acquisition of an optimal allocation scheme with greater benefits. This verifies the effectiveness and superiority of the proposed model and algorithm in maximizing combat mission benefits.