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A new Taxonomy of Continuous Global Optimization Algorithms

  • Surrogate-based optimization and nature-inspired metaheuristics have become the state of the art in solving real-world optimization problems. Still, it is difficult for beginners and even experts to get an overview that explains their advantages in comparison to the large number of available methods in the scope of continuous optimization. Available taxonomies lack the integration of surrogate-based approaches and thus their embedding in the larger context of this broad field. This article presents a taxonomy of the field, which further matches the idea of nature-inspired algorithms, as it is based on the human behavior in path finding. Intuitive analogies make it easy to conceive the most basic principles of the search algorithms, even for beginners and non-experts in this area of research. However, this scheme does not oversimplify the high complexity of the different algorithms, as the class identifier only defines a descriptive meta-level of the algorithm search strategies. The taxonomy was established by exploring and matching algorithm schemes, extracting similarities and differences, and creating a set of classification indicators to distinguish between five distinct classes. In practice, this taxonomy allows recommendations for the applicability of the corresponding algorithms and helps developers trying to create or improve their own algorithms.

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Author:Jörg Stork, A.E. Eiben, Thomas Bartz-Beielstein
Series (Serial Number):CIplus (4/2018)
Document Type:Article
Year of Completion:2018
Release Date:2018/09/26
Tag:Evolutionary Computation; Metaheuristics; Optimization; Surrogate; Taxonomy
GND Keyword:Globale Optimierung; Taxonomie; Evolutionärer Algorithmus; Metaheuristik
Page Number:52
Institutes and Central Facilities:Fakultät für Informatik und Ingenieurwissenschaften (F10)
Dewey Decimal Classification:000 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik
Open Access:Open Access
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International