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。