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.

通过将高斯压缩搜索（GCS）与不需求导的优化中的 Powell 方法进行混合，本研究提出了一种增强优化性能的新方法。通过实验发现，将 GCS 与某些传统的不需求导的优化方法相结合，不仅可以显著提升性能，同时保留了每种方法的优势，为优化复杂系统以及在各种应用中寻找最优解打开了新的可能性。

增强优化性能：高斯融合搜索与 Powell 方法的无导数优化中的新型混合算法

Enhancing Optimization Performance: A Novel Hybridization of Gaussian  Crunching Search and Powell's Method for Derivative-Free Optimization

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.

优化方法及其应用，介绍了一种新的优化方法 —— 高斯压缩搜索（GCS），通过对高斯分布中粒子行为的仿真，GCS 旨在高效地探索解空间并收敛于全局最优解。通过实验评估及与其他优化方法的比较，突出了 GCS 的优势和特点，为对优化感兴趣的研究人员、实践者和学生提供了有价值的资源，深入了解高斯压缩搜索作为一种新的有前景的方法的发展和潜力。