Comparing the Performance of Domain Transform-based Differential Evolution with Recent CEC Competition Winners on the CEC2025 Numerical Optimization

Differential evolution is one of the most promising evolutionary algorithms for real-parameter single-objective optimization. It has usually been hybridized with another competitive algorithm, the CMA-ES to boost the overall performance in the recent CEC competitions. In this paper, we compare the performance of a recently proposed domain transform-based differential evolution with the CEC competition winners on the CEC2025 benchmark functions. A new performance evaluation method, the U-score approach which could measure both the speed and accuracy of stochastic optimizers is adopted in the comparison. The results show that the domain transform-based differential evolution achieves the best performance against the winners which are usually hybrid methods. This study indicates that a pure DE could also be competitive to or even outperform hybrid algorithms. Besides guaranteeing the performance, it also simplifies algorithmic designs.