To expand mission capabilities needed for exploration of the Solar System, optimal low-thrust trajectories must be found. However, low-thrust, multiple gravity-assist trajectories pose significant optimization challenges because of their expansive, multimodal design space. Here, a novel technique is developed for global, low-thrust, interplanetary trajectory optimization through the hybridization of a genetic algorithm and a gradient-based direct method (GALLOP). The hybrid algorithm combines the effective global search capabilities of a genetic algorithm with the robust convergence and constraint handling of the local, calculus-based direct method. The automated approach alleviates the difficulty and biases of initial guess generation and provides near globally optimal solutions. The technique is applied to several complex low-thrust, gravity-assist trajectory scenarios, generating previously unpublished optimums.
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