Multiobjective Optimization of Low-thrust Trajectories Using a Genetic Algorithm Hybrid

In low-thrust, gravity-assist trajectory design, two objectives are often equally important: maximization of final spacecraft mass and minimization of time-of-flight. Generally, these objectives are coupled and competing. Designing the trajectory that is best-suited for a mission typically requires a compromise between the objectives. However, optimizing even a single objective in the complex design space of low-thrust, gravity-assist trajectories is difficult. The technique in this development hybridizes a multiobjective genetic algorithm (NSGA-II) and an efficient, calculus-based direct method (GALLOP). The hybrid algorithm capitalizes on the benefits of both methods to generate a representation of the Pareto front of near-globally optimal solutions.