INTERPLANETARY TRAJECTORY OPTIMIZATION USING A GENETIC ALGORITHM

Robust optimization of interplanetary trajectories is difficult becaus e the domain is typically multimodal and discontinuous. Genetic algori thms are often successful in such domains. Therefore, a genetic algori thm has been linked to a patched-conic mission analysis code to assess its performance for interplanetary problems. It is compared with grid search techniques, which are commonly used in the initial exploration of interplanetary concepts. This new method out-performs grid search in all test problems considered to date. The genetic algorithm's perfo rmance superiority is shown to increase with the number of design vari ables. When a sharing function is included, the genetic algorithm is a ble to identify several local minima. Performance was influenced by th e topology of the search space: large infeasible regions increased the difficulty of the problem. The genetic algorithm is able to explore d ifferent mission concepts simultaneously, allowing simpler problem for mulation for the user.