\begin{abstract}
Previously we have achieved significant performance improvements in model optimization using
our KEYS2 search algorithm on defect detection 
and prevention (DDP) models from JPL. In these models we seek 
the reduction of software development costs while maximizing requirements
attainment. Unfortunately, as these models grow larger, 
the search space typically grows at an exponential rate, and the 
demand for faster optimizations methods increases. In this study 
we generate large risk models and attempt to speed up the KEYS2 
search engine. After some experimentation and analysis we arrived
at a fairly simple optimization technique that maintains the level of attainment and
cost reductions of KEYS2 but also significantly reduces the run 
time on larger models so the engineers making decisions based on model 
simulations do not have to spend their valuable time waiting for the 
results of the tool to come back.

{\bf Keywords:} Model optimization, AI search
\end{abstract}
