Design of Kill Chain Reconstruction Method Based on Particle Swarm Optimization Algorithm

In this paper, the problem of rapidly reconstructing the kill chain for tasks due to the frequent changes of battlefield situation in the current beyond-visual-range cooperative air combat is studied. The kill chain is approximated as a series of action combinations arranged in chronological order for the target. Based on the idea of sequential decision-making, the reconstruction problem is transformed into a task redistribution problem at each stage of the kill chain. When the reconstruction condition is triggered, the full-chain reconstruction is performed, which reduces the complexity of the problem. By using the improved particle swarm optimization algorithm and relying on the kill chain effectiveness evaluation as the objective function, the combat resources and combat tasks are screened and matched. Through simulation experiments, the results obtained by the algorithm are compared with the results obtained by the enumeration method, which verifies that the algorithm can improve the speed of problem solving while ensuring the effectiveness of the results.