Multiple gravity-assisted trajectory optimization using a hybrid method

Global optimization methods have been increasing under consideration for complicated trajectory optimization problems. A hybrid optimization method combining the global optimal properties of genetic algorithms with the local optimal characteristic of sequential quadratic programming has been developed. The genetic algorithm initially searches the parameter space for candidate gravity-assisted planetary. The best parameter of the genetic algorithm is submitted as an initial parameter set to the sequential quadratic programming module for improvement. This hybrid optimization method was applied to optimize and design transfer trajectory of Cassini mission. The effectiveness of this method is demonstrated by the rapid solution of interplanetary trajectory problems that involve complex features such as multiple gravity assist consideration.