Resource Scheduling Method for Space-Based Early Warning System Based on GA-PSO Algorithm

To solve the resource scheduling problem of satellites in space-based early warning system, we build a resource scheduling model based on the characteristics of the early warning tasks and their decomposition. We propose a hybrid GA-PSO optimization algorithm, which combines the advantages of genetic algorithm (GA) and particle swarm optimization (PSO), to solve the resource scheduling problem for the space-based early warning system. The algorithm introduces the genetic operators of GA into PSO algorithm to improve the search ability of PSO algorithm, while solving the problem of particles coding and decoding. The experimental results demonstrate that the proposed algorithm can solve resource scheduling problem of space-based early warning system for multi-target detection effectively and space-based early warning missions, and the scheduling results is better than GA and PSO.