  As the pace of AI technology continues to accelerate, more tools have become
available to researchers to solve longstanding problems, Hybrid approaches
available today continue to push the computational limits of efficiency and
precision. One of such problems is the inverse kinematics of redundant systems.
This paper explores the complexities of a 7 degree of freedom manipulator and
explores 13 optimization techniques to solve it. Additionally, a novel approach
is proposed to contribute to the field of algorithmic research. This was found
to be over 200 times faster than the well-known traditional Particle Swarm
Optimization technique. This new method may serve as a new field of search that
combines the explorative capabilities of Machine Learning with the exploitative
capabilities of numerical methods.
