PSO-GA Based Fuel Optimization Algorithm for High Orbit One-to-Many Spacecraft Rendezvous

In response to the fuel consumption problem in one-to-many spacecraft rendezvous missions, a combined optimization algorithm integrating Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) considering J2 perturbation is proposed in this paper. Firstly, a spacecraft model accounting for J2 perturbation is established, and a method based on Jacobi matrix iteration is designed to implement the double-impulse optimal multiple-revolution Lambert algorithm as the spacecraft maneuver strategy. This strategy provides a more fuel-efficient approach for spacecraft rendezvous. Secondly, to address the fuel optimization problem for rendezvous missions, a fitness function for fuel consumption in one-to-many missions is designed, and the PSO-GA optimization-based algorithm is introduced. The PSO-GA algorithm utilizes PSO to generate a larger initialization population. It selects some optimal individuals as the initialization population for GA through a screening mechanism, thus improving convergence speed and avoiding local optimal solutions. Finally, through a series of simulation experiments and comparisons, the superiority and effectiveness of the proposed algorithm are verified.