This paper presents a comprehensive survey of benchmark functions for global optimization problems. The authors compile a collection of 175 benchmark functions for unconstrained optimization problems, which are categorized based on their properties such as modality, separability, and valley landscape. These functions are designed to test the performance of optimization algorithms by providing diverse scenarios, including unimodal, multimodal, separable, and non-separable functions. The functions are described in detail, including their mathematical expressions, domains, and global minima. The study emphasizes the importance of using a diverse set of benchmark functions to evaluate the effectiveness of optimization algorithms in various challenging scenarios. The authors also discuss the characteristics of test functions, such as modality, basins, valleys, and dimensionality, which are crucial for assessing the performance of optimization algorithms. The paper highlights the need for a standardized set of benchmark functions to ensure fair and unbiased comparisons among different optimization algorithms. The compiled set of functions is intended to serve as a valuable resource for researchers and practitioners in the field of global optimization.This paper presents a comprehensive survey of benchmark functions for global optimization problems. The authors compile a collection of 175 benchmark functions for unconstrained optimization problems, which are categorized based on their properties such as modality, separability, and valley landscape. These functions are designed to test the performance of optimization algorithms by providing diverse scenarios, including unimodal, multimodal, separable, and non-separable functions. The functions are described in detail, including their mathematical expressions, domains, and global minima. The study emphasizes the importance of using a diverse set of benchmark functions to evaluate the effectiveness of optimization algorithms in various challenging scenarios. The authors also discuss the characteristics of test functions, such as modality, basins, valleys, and dimensionality, which are crucial for assessing the performance of optimization algorithms. The paper highlights the need for a standardized set of benchmark functions to ensure fair and unbiased comparisons among different optimization algorithms. The compiled set of functions is intended to serve as a valuable resource for researchers and practitioners in the field of global optimization.