Think I'm gonna call it for now. May reassess, but almost completely done!

LaTeX/conclusion.tex

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+\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.
+
+		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. However, both results show very interesting trajectories that could indicate some
+		favorable possibilities for such a mission profile. Each of these trajectories should be
+		within the capabilities of existing launch vehicles in terms of $C_3$.
+
+	\section{Recommendations for Future Work}\label{improvement_section}
+
+		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.