New contributions in the field of iterative optimisation heuristics are often made in an iterative manner. Novel algorithmic ideas are not proposed in isolation, but usually as an extension of a preexisting algorithm. Although these contributions are often compared to the base algorithm, it is challenging to make fair comparisons between larger sets of algorithm variants. This happens because even small changes in the experimental setup, parameter settings, or implementation details can cause results to become incomparable. Modular algorithms offer a way to overcome these challenges. By implementing the algorithmic modifications into a common framework, many algorithm variants can be compared, while ensuring that implementation details match in all versions. In this work, we propose a version of a modular framework for the popular Differential Evolution (DE) algorithm. We show that this modular approach not only aids in comparison, but also allows for a much more detailed exploration of the space of possible DE variants. This is illustrated by showing that tuning the settings of modular DE vastly outperforms a set of commonly used DE versions which have been recreated in our framework. We then investigate these tuned algorithms in detail, highlighting the relation between modules and performance on particular problems.

翻译：在迭代优化启发式算法领域，新贡献往往以迭代方式呈现。新颖的算法思想并非孤立提出，而通常是作为现有算法的扩展。尽管这些贡献常与基准算法进行比较，但在更大的算法变体集合间进行公平比较极具挑战性。这是由于实验设置、参数配置或实现细节的微小差异都可能导致结果不可比。模块化算法为此提供了解决方案：通过将算法改进整合至统一框架，可在确保所有版本实现细节一致的前提下，对多种算法变体进行对比。本研究为著名的差分进化（DE）算法提出了一种模块化框架版本。我们证明，这种模块化方法不仅有助于比较，还能更细致地探索DE变体的可能空间。研究表明，通过调优模块化DE的配置，其表现显著优于在我们框架中重构的一组常用DE算法变体。最后对这些调优后的算法进行深入分析，揭示了特定问题上模块与性能之间的关联。