Low-thrust Orbit Transfer Optimization Using Genetic Search

Most techniques for solving dynamic optimization problems involve a series of gradient computations and one-dimensional searches at some point in the optimization process. A large class of problems, however, does not possess the necessary smoothness properties that such algorithms require for good convergence. Even when smoothness conditions are met, poor initial guesses at the solution often result in convergence to local minima or even a lack of convergence altogether. For such cases, genetic search techniques can be used to obtain a solution. In this paper, trajectory optimization using genetic search methods is illustrated by solving a complex, nonlinear problem involving low-thrust orbit transfer.