A Parallel Processing and Diversified-Hidden-Gene-Based Genetic Algorithm Framework for Fuel-Optimal Trajectory Design for Interplanetary Spacecraft Missions

Abstract

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 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.