\chapter{Conclusion} \label{conclusion}

		This thesis explored an approach for automating the initial analysis and discovery of useful
		interplanetary, low-thrust trajectories including the difficult task of optimizing the flyby
		parameters. This makes the mission designer's job significantly simpler in that they can
		simply explore a number of different flyby selection options in order to get a good
		understanding of the mission scope and search space for a given spacecraft, launch window,
		and target. The aim of this exploration was to examine whether a Monotonic Basin Hopping
		algorithm utilizing an inner-loop NLP solver would be an appropriate choice for the
		particular problem of low-thrust interplanetary trajectory optimization.

		In performing this examination, two results were selected for further analysis. These
		results are outlined in Table~\ref{results_table}. As can be seen in the table, both
		resulting trajectories have trade-offs in mission length, launch energy, fuel usage, and
		more. Each of these trajectories appear to be within the capabilities of existing launch
		vehicles in terms of $C_3$.

		In the course of producing this algorithm, a large number of improvement possibilities were
		noted. This work was based, in large part, on the work of Jacob Englander in a number of
		papers\cite{englander2014tuning}\cite{englander2017automated} \cite{englander2012automated}
		in which they explored the hybrid optimal control problem of multi-objective low-thrust
		interplanetary trajectories.

		In light of this, there are a number of additional approaches that Englander took in
		preparing their algorithm that were not implemented here in favor of reducing complexity and
		time constraints. For instance, many of the Englander papers explore the concept of an outer
		loop that utilizes a genetic algorithm to compare many different flyby planet choices
		against each other.

		Further improvements, in the name of performance stem from the field of computer science. An
		evolutionary algorithm such as the one proposed by Englander would benefit from high levels
		of parallelization. Therefore, it would be worth considering a GPU-accelerated or even
		cluster-computing capable implementation of the monotonic basin hopping algorithm.

		Finally, the monotonic basin hopping algorithm as currently written provides no guarantees
		of actual global optimization. Generally optimization is achieved by running the algorithm
		until it fails to produce newer, better trajectories for a sufficiently long time. But it
		would be worth investigating the robustness of the NLP solver as well as the robustness of
		the MBH algorithm basin drilling procedures in order to quantify the search granularity
		needed to completely traverse the search space.
