Enhancing UAV swarm mission planning with a weighted graph attention network-based genetic algorithm

Mission planning for UAV swarms is an NP-hard combinatorial optimization problem. Genetic algorithms find approximate solutions to this problem within an acceptable computational time. However, in complex real combat scenarios, the computation of individual fitness becomes exceedingly difficult, which severely restricts the efficiency of genetic algorithms in exploring the solution space for mission planning. To solve this problem, this study proposes an improved genetic algorithm based on a weighted graph attention network. This method encodes the chromosomes of individuals into a graph structure and makes predictions about the fitness of individuals by introducing a graph neural network, which ensures prediction accuracy while reducing the complexity of fitness calculation. To eliminate the prediction error of the graph neural network as much as possible, the actual fitness value is employed for corrections when required.