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Nature inspired optimization algorithms or simply variations of metaheuristics?
Management and Decision Engineering Laboratory, Department of Financial and Management Engineering, School of Engineering, University of the Aegean, Chios, Greece.ORCID iD: 0000-0002-1319-513X
Management and Decision Engineering Laboratory, Department of Financial and Management Engineering, School of Engineering, University of the Aegean, Chios, Greece.
2021 (English)In: Artificial Intelligence Review, ISSN 0269-2821, E-ISSN 1573-7462, Vol. 54, p. 1841-1862Article in journal (Refereed) Published
Abstract [en]

In the last decade, we observe an increasing number of nature-inspired optimization algorithms, with authors often claiming their novelty and their capabilities of acting as powerful optimization techniques. However, a considerable number of these algorithms do not seem to draw inspiration from nature or to incorporate successful tactics, laws, or practices existing in natural systems, while also some of them have never been applied in any optimization field, since their first appearance in literature. This paper presents some interesting findings that have emerged after the extensive study of most of the existing nature-inspired algorithms. The need for irrationally introducing new nature inspired intelligent (NII) algorithms in literature is also questioned and possible drawbacks of NII algorithms met in literature are discussed. In addition, guidelines for the development of new nature-inspired algorithms are proposed, in an attempt to limit the misleading appearance of variation of metaheuristics as nature inspired optimization algorithms. 

Place, publisher, year, edition, pages
Springer, 2021. Vol. 54, p. 1841-1862
Keywords [en]
AI and optimization, Evaluation of algorithm’s innovation, Guidelines for nature-inspired algorithms, Nature-inspired intelligent (NII) algorithms, Biomimetics, Heuristic algorithms, Meta heuristics, Natural systems, Nature inspired algorithms, Optimization algorithms, Optimization techniques, Optimization
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hj:diva-63831DOI: 10.1007/s10462-020-09893-8ISI: 000562343400001Scopus ID: 2-s2.0-85089734985OAI: oai:DiVA.org:hj-63831DiVA, id: diva2:1845914
Available from: 2024-03-20 Created: 2024-03-20 Last updated: 2024-03-20Bibliographically approved

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Tzanetos, Alexandros

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