The recent and upcoming availability of exascale computational resources has created renewed interest in high-performance automated software frameworks for the development of molecular reaction networks. A crucial step in such frameworks is the determination of saddle point geometries for the calculation of reaction rate coefficients under transition state theory. Automation of saddle point optimization requires robust optimization algorithms that converge quickly and reliably to saddle points. This has typically been a source of difficulty in the development of automated frameworks due to the computational expense involved and the rate of convergence failure many algorithms exhibit. Researchers often have to adjust saddle point initial guess structures manually to avoid pathological configurations and improve convergence of the optimization algorithm. There is therefore great need for development of new saddle point optimization algorithms whose reliability and performance is suitable for deployment in automated frameworks.