A new Taxonomy of Continuous Global Optimization Algorithms

TL;DR

This paper introduces a comprehensive taxonomy of continuous global optimization algorithms, categorizing them based on their search strategies, which helps practitioners understand their differences, similarities, and suitable applications.

Contribution

It provides a novel taxonomy that classifies algorithms by their search components, offering insights into their design and guiding practical application choices.

Findings

Classification indicators distinguish algorithm classes

Deeper understanding of search strategy components

Guidelines for algorithm applicability

Abstract

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 design optimization. The extracted features of components or 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.