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This research paper presents a novel approach to enhance optimizationperformance through the hybridization of Gaussian Crunching Search (GCS) andPowell's Method for derivative-free optimization. While GCS has shown promisein overcoming challenges faced by traditional derivative-free optimizationmethods [1], it may not always excel in finding the local minimum. On the otherhand, some traditional methods may have better performance in this regard.However, GCS demonstrates its strength in escaping the trap of local minima andapproaching the global minima. Through experimentation, we discovered that bycombining GCS with certain traditional derivative-free optimization methods, wecan significantly boost performance while retaining the respective advantagesof each method. This hybrid approach opens up new possibilities for optimizingcomplex systems and finding optimal solutions in a range of applications.

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