Optimal finite-thrust rendezvous trajectories found via particle swarm algorithm

The particle swarm optimization technique is a population-based stochastic method developed in recent years and successfully applied in several fields of research. It represents a very intuitive (and easy to program) methodology for global optimization, inspired by the behavior of bird flocks while searching for food. The particle swarm optimization technique attempts to take advantage of the mechanism of information sharing that affects the overall behavior of a swarm, with the intent of determining the optimal values of the unknown parameters of the problem under consideration. This research applies the technique to determining optimal continuous-thrust rendezvous trajectories in a rotating Euler-Hill frame. Hamiltonian methods are employed to translate the related optimal control problems into parameter optimization problems. Thus the parameters sought by the swarming algorithm are primarily the initial values of the costates and the final time. The algorithm at hand is extremely easy to program. Nevertheless, it proves to be effective, reliable, and numerically accurate in solving the rendezvous optimization problems considered in this work.