An Evolutionary Algorithm for Low-thrust Orbital Transfers

Open Access
Hashimoto, Kaomi Cristina
Area of Honors:
Aerospace Engineering
Bachelor of Science
Document Type:
Thesis Supervisors:
  • Robert Graham Melton, Thesis Supervisor
  • George A Lesieutre, Honors Advisor
  • David Bradley Spencer, Faculty Reader
  • Particle Swarm Optimization
  • Low-Thrust
  • Orbital Transfers
  • Astrodynamics
Flocks of birds seem to travel randomly when searching for food, but each bird is actually communicating its route with the others in order to find the optimal path to their location. The particle swarm optimization (PSO) technique is a stochastic method that utilizes this theory to optimize an unknown parameter by analyzing the behavior of a swarm of particles. In this thesis, the PSO technique has been applied to create an evolutionary algorithm that analyzes low-thrust transfers between two co-planar, elliptical orbits, both coaxial and non-coaxial. Transfers are modeled as two thrust arcs with an intermediary coast arc. The algorithm determines the optimal thrust pointing angles, the initial true anomaly of the spacecraft, the duration of each thrust arc, and the change in eccentric anomaly of the coast arc that result in the greatest final-to-initial mass ratio. While the method is successful in producing the optimal position vector for the transfer, it is imperative that the penalties and penalty conditions placed on the objective function constraints are also optimized. The PSO algorithm is successful in determining a transfer trajectory between non-coaxial elliptical orbits, but the accuracy of these results is still unknown.