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

This paper 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, can execute on any High-Performance Computing (HPC) platform with the adoption of the portable Message Passing Interface (MPI) standard. New procedures are developed for determining trajectories in legs of the flight from the launch planet, 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 ΔV, and does not include the time of flight. The proposed algorithm is proven superior to the classical genetic algorithm both in terms of convergence characteristics for the cost function and the depth of the search space explored.