This paper provides a comprehensive survey and experimental analysis of recent advances in Differential Evolution (DE), a simple and efficient optimizer, particularly for continuous optimization problems. The authors classify DE modifications into two main categories: algorithms that integrate additional components within the DE structure and those that employ a modified DE structure. Four representative algorithms from each category are described in detail, and their performance is compared using benchmark problems in both low and high-dimensional cases. The study highlights the importance of incorporating additional search moves to enhance DE's performance, such as population size reduction and scale factor local search. Additionally, global and local neighborhood search, as well as self-adaptive control parameter schemes, are identified as promising modifications. The paper concludes that while no single algorithm consistently outperforms others, the integration of additional components and the proper use of randomization are crucial for significant improvements in DE's functionality.This paper provides a comprehensive survey and experimental analysis of recent advances in Differential Evolution (DE), a simple and efficient optimizer, particularly for continuous optimization problems. The authors classify DE modifications into two main categories: algorithms that integrate additional components within the DE structure and those that employ a modified DE structure. Four representative algorithms from each category are described in detail, and their performance is compared using benchmark problems in both low and high-dimensional cases. The study highlights the importance of incorporating additional search moves to enhance DE's performance, such as population size reduction and scale factor local search. Additionally, global and local neighborhood search, as well as self-adaptive control parameter schemes, are identified as promising modifications. The paper concludes that while no single algorithm consistently outperforms others, the integration of additional components and the proper use of randomization are crucial for significant improvements in DE's functionality.