Interplanetary flyby mission optimization using a hybrid global-local search method

A hybrid optimization approach has been developed combining the global search properties of genetic algorithms with the local search characteristics of recursive quadratic programming, The genetic algorithm initially surveys the parameter space for candidate planetary encounter and maneuver occurrence times. The best candidate of the genetic algorithm parameter population is submitted as an initial parameter set to the recursive quadratic programming module for refinement. This hybrid optimizer was applied to ballistic Earth-Venus-Earth (EVE) and Earth-Mars-Earth (EME) flybys to minimize weighted mission Delta V constrained by Earth return energy and mission time of flight inequalities. These mission classes were chosen for their potential to demonstrate Mars transportation vehicle technology. Initial experiments with a time of flight limit of 480 days and an Earth-return energy limit of 14 km/s provided EME and EVE solutions with negligible Delta V at the respective flyby encounters. The hybrid optimizer required two orders of magnitude fewer trajectory evaluations to produce results equivalent to a grid search for this mission. The hybrid optimizer results indicate that a human Venus flyby transportation verification mission is superior to a Mars flyby mission.