Surrogate-based optimization, nature-inspired metaheuristics, and hybrid combinations have become state of the art in algorithm design for solving real-world optimization problems. Still, it is difficult for practitioners to get an overview that explains their advantages in comparison to a large number of available methods in the scope of optimization. Available taxonomies lack the embedding of current approaches in the larger context of this broad field. This article presents a taxonomy of the field, which explores and matches algorithm strategies by extracting similarities and differences in their search strategies. A particular focus lies on algorithms using surrogates, nature-inspired designs, and those created by automatic algorithm generation. The extracted features of algorithms, their main concepts, and search operators, allow us to create a set of classification indicators to distinguish between a small number of classes. The features allow a deeper understanding of components of the search strategies and further indicate the close connections between the different algorithm designs. We present intuitive analogies to explain the basic principles of the search algorithms, particularly useful for novices in this research field. Furthermore, this taxonomy allows recommendations for the applicability of the corresponding algorithms.

基于代理的优化，自然启发式元启发法和混合组合已成为解决现实世界优化问题的算法设计中的最新技术。尽管如此，从业人员仍然很难获得一个概述来解释他们与优化范围内的大量可用方法相比的优势。在这个广阔领域的更大范围内，可用的分类法缺乏当前方法的嵌入。本文介绍了该领域的分类法，它通过提取搜索策略的相似性和差异来探索和匹配算法策略。特别关注使用代理，自然灵感设计以及由自动算法生成创建的代理的算法。算法的提取特征，主要概念和搜索运算符，允许我们创建一组分类指标以区分少量的类。这些功能使您可以更深入地了解搜索策略的组成部分，并进一步表明不同算法设计之间的紧密联系。我们提供直观的类比来解释搜索算法的基本原理，这对本研究领域的新手特别有用。此外，该分类法允许对相应算法的适用性提出建议。对于这个研究领域的新手来说特别有用。此外，该分类法允许对相应算法的适用性提出建议。对于这个研究领域的新手来说特别有用。此外，该分类法允许对相应算法的适用性提出建议。