Interplanetary Flyby Mission Optimization Using a Hybrid Global-Local Search Method

Ahybridoptimizationapproachhasbeen developed combiningthe globalsearch 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 reŽ nement. This hybrid optimizer was applied to ballistic Earth–Venus–Earth (EVE) and Earth–Mars–Earth (EME)  ybys to minimize weighted mission D V constrained by Earth return energy and mission time of  ight inequalities. These mission classes were chosen for their potential to demonstrate Mars transportation vehicle technology. Initial experiments with a time of  ight limit of 480 days and an Earth-return energy limit of 14 km/s provided EME and EVE solutions with negligible D V at the respective  yby 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  yby transportation veriŽ cation mission is superior to a Mars  yby mission.