A comprehensive database of Nature-Inspired Algorithms

Alexandros Tzanetos; Iztok Fister Jr; Georgios Dounias

doi:10.1016/j.dib.2020.105792

A comprehensive database of Nature-Inspired Algorithms

Data Brief. 2020 Jun 2:31:105792. doi: 10.1016/j.dib.2020.105792. eCollection 2020 Aug.

Authors

Alexandros Tzanetos¹, Iztok Fister Jr², Georgios Dounias¹

Affiliations

¹ Management and Decision Engineering Laboratory, Department of Financial and Management Engineering, School of Engineering, University of the Aegean, Greece.
² University of Maribor, Faculty of Electrical Engineering and Computer Science, Institute of Informatics, Maribor, Slovenia.

Abstract

These data contain a comprehensive collection of all Nature-Inspired Algorithms. This collection is a result of two corresponding surveys, where all Nature-Inspired Algorithms that have been published to-date were gathered and preliminary data acquired. The rapidly increasing number of nature-inspired approaches makes it hard for interested researchers to keep up. Moreover, a proper taxonomy is necessary, based on specific features of the algorithms. Different taxonomies and useful insight into the application areas that the algorithms have coped with is given through these data. This article provides a detailed description of the above mentioned collection.

Keywords: Bio-inspired algorithms; Evolutionary algorithms; Evolutionary computing; Nature-inspired algorithms; Nature-inspired computing; Swarm intelligence.