Enhancing Optimization Performance: A Novel Hybridization of Gaussian
  Crunching Search and Powell's Method for Derivative-Free Optimization
Benny Wong
This research paper presents a novel approach to enhance optimization
performance through the hybridization of Gaussian Crunching Search (GCS) and
Powell's Method for derivative-free optimization. While GCS has shown promise
in overcoming challenges faced by traditional derivative-free optimization
methods [1], it may not always excel in finding the local minimum. On the other
hand, some traditional methods may have better performance in this regard.
However, GCS demonstrates its strength in escaping the trap of local minima and
approaching the global minima. Through experimentation, we discovered that by
combining GCS with certain traditional derivative-free optimization methods, we
can significantly boost performance while retaining the respective advantages
of each method. This hybrid approach opens up new possibilities for optimizing
complex systems and finding optimal solutions in a range of applications.