A Parallel Processing and Diversified-Hidden-Gene-based Genetic Algorithm Framework for Fuel-Optimal Trajectory Design for Interplanetary Spacecraft Missions
Master of Science (MS)
diverse genetic algorithm, high performance computing, interplanetary mission design, parallel computing, trajectory optimization
This thesis proposes a new parallel computing genetic algorithm framework for designing fuel-optimal trajectories for interplanetary spacecraft missions. The framework can capture the deep search space of the problem with the use of a fixed chromosome structure and hidden-genes concept, can explore the diverse set of candidate solutions with the use of the adaptive and twin-space crowding techniques and, can execute on any high-performance computing (HPC) platform with the adoption of the portable message passing interface (MPI) standard. The algorithm is implemented in C++ with the use of the MPICH implementation of the MPI standard. The algorithm uses a patched-conic approach with two-body dynamics assumptions. New procedures are developed for determining trajectories in the V-infinity-leveraging legs of the flight from the launch and non-launch planets and, deep-space maneuver legs of the flight from the launch and non-launch planets. The chromosome structure maintains the time of flight as a free parameter within certain boundaries. The fitness or the cost function of the algorithm uses only the mission $\Delta V$, and does not include time of flight. The optimization is conducted with two variations for the minimum mission gravity-assist sequence, the 4-gravity-assist, and the 3-gravity-assist, with a maximum of 5 gravity-assists allowed in both the cases. The optimal trajectories discovered using the framework in both of the cases demonstrate the success of this framework.
Somavarapu, Dhathri Harsha, "A Parallel Processing and Diversified-Hidden-Gene-based Genetic Algorithm Framework for Fuel-Optimal Trajectory Design for Interplanetary Spacecraft Missions" (2017). Master's Theses. 4822.