Adaptive Differential Evolution (DE) methods are currently among the most efficient Evolutionary Algorithms. In the recent years different Success-History-Based Adaptive Differential Evolution algorithms (SHADE), often with linear population size reduction (commonly known as L-SHADE), have won numerous Competitions in Evolutionary Computation. Since 2014, the number and the variety of SHADE or L-SHADE-based algorithms flourished, encompassing novel operators and procedures. However, it is unclear which new SHADE/L-SHADE operators and procedures are the most successful, or efficient, for specific kinds of problems. After more than a decade of rapid development, some large-scale empirical tests are needed to select the best SHADE/L-SHADE-based algorithms for different purposes. This paper aims at a wide-scale inter-comparison between 32 SHADE/L-SHADE-based variants on large sets of various-dimensional benchmarks and on numerous real-world problems. We point at SHADE/L-SHADE-based algorithms that perform best for low-, or for high-dimensional problems. We determine variants that outperform others on simple problems, and those that perform best for more difficult tasks. Finally, we analyze which variants are best-suited for real-world applications, considering different computational budgets. Results indicate that much different SHADE/L-SHADE-based algorithms perform best for real-world problems than for numerical benchmark functions. Also, different algorithms may be recommended for higher, than for lower-dimensional problems, and other methods perform better for difficult problems than for unimodal ones. This discrepancy poses a challenge for choosing the appropriate algorithm for the specific application, and casts doubts on the classical way of justifying the introduction of novel variants.
Piotrowski et al. (2026) studied this question.