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A comprehensive database of Nature-Inspired Algorithms
Management and Decision Engineering Laboratory, Department of Financial and Management Engineering, School of Engineering, University of the Aegean, Greece.ORCID iD: 0000-0002-1319-513X
University of Maribor, Faculty of Electrical Engineering and Computer Science, Institute of Informatics, Maribor, Slovenia.
Management and Decision Engineering Laboratory, Department of Financial and Management Engineering, School of Engineering, University of the Aegean, Greece.
2020 (English)In: Data in Brief, E-ISSN 2352-3409, Vol. 31, article id 105792Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2020. Vol. 31, article id 105792
Keywords [en]
Bio-inspired algorithms, Evolutionary algorithms, Evolutionary computing, Nature-inspired algorithms, Nature-inspired computing, Swarm intelligence
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hj:diva-63834DOI: 10.1016/j.dib.2020.105792ISI: 000569216000031PubMedID: 32577446Scopus ID: 2-s2.0-85088461324OAI: oai:DiVA.org:hj-63834DiVA, id: diva2:1845920
Available from: 2024-03-20 Created: 2024-03-20 Last updated: 2024-04-02Bibliographically approved

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