(EDGE 2026) A Review of the Differential Evolution Algorithm: Mutation and Crossover Strategies, Improved Variants

Optimization problems exist widely in various fields. To address these optimization
problems, extensive investigations of optimization methods have been conducted. Conventional search and optimization methods encounter challenges in complex problems. Inspired by the principles of nature, researchers developed a set of evolutionary algorithms. As one of evolutionary algorithms, the algorithm known as Differential Evolution is both uncomplicated and effective. The frame is straightforward with minimal parameters, making it easy to implement. Meanwhile, the DE algorithm exhibits high efficiency and strong robustness. In addition, owing to the excellent performance and scalability of DE, DE has found extensive applications in theoretical research and
various engineering practice. Many efforts are made to improve Differential Evolution Algorithm. Some variants of DE used to tackle with different problems are presented. This article provides a description of some kinds of improvement for Differential Evolution Algorithm. Moreover, There are several worthy research directions regarding DE for future work.