  Optimization methods are essential in solving complex problems across various
domains. In this research paper, we introduce a novel optimization method
called Gaussian Crunching Search (GCS). Inspired by the behaviour of particles
in a Gaussian distribution, GCS aims to efficiently explore the solution space
and converge towards the global optimum. We present a comprehensive analysis of
GCS, including its working mechanism, and potential applications. Through
experimental evaluations and comparisons with existing optimization methods, we
highlight the advantages and strengths of GCS. This research paper serves as a
valuable resource for researchers, practitioners, and students interested in
optimization, providing insights into the development and potential of Gaussian
Crunching Search as a new and promising approach.
